U.S. patent application number 15/186223 was filed with the patent office on 2016-12-29 for match fix-up to remove matching documents.
The applicant listed for this patent is MICROSOFT TECHNOLOGY LICENSING, LLC. Invention is credited to ROBERT LOVEJOY GOODWIN, MICHAEL JOSEPH HOPCROFT, UTKARSH JAIN, FAN WANG.
Application Number | 20160378796 15/186223 |
Document ID | / |
Family ID | 56292973 |
Filed Date | 2016-12-29 |
United States Patent
Application |
20160378796 |
Kind Code |
A1 |
HOPCROFT; MICHAEL JOSEPH ;
et al. |
December 29, 2016 |
MATCH FIX-UP TO REMOVE MATCHING DOCUMENTS
Abstract
The technology described herein provides for a match fix-up
stage that removes matching documents identified for a search query
that don't actually contain terms from the search query. A
representation of each document (e.g., a forward index storing a
list of terms for each document) is used to identify valid matching
documents (i.e., documents containing terms from the search query)
and invalid matching documents (i.e., documents that don't contain
terms from the search query). Any invalid matching documents are
removed from further processing and ranking for the search
query.
Inventors: |
HOPCROFT; MICHAEL JOSEPH;
(KIRKLAND, WA) ; GOODWIN; ROBERT LOVEJOY; (MERCER
ISLAND, WA) ; WANG; FAN; (REDMOND, WA) ; JAIN;
UTKARSH; (KIRKLAND, WA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
MICROSOFT TECHNOLOGY LICENSING, LLC |
Redmond |
WA |
US |
|
|
Family ID: |
56292973 |
Appl. No.: |
15/186223 |
Filed: |
June 17, 2016 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
62183556 |
Jun 23, 2015 |
|
|
|
Current U.S.
Class: |
707/692 |
Current CPC
Class: |
G06F 16/316 20190101;
G06F 16/334 20190101; G06F 16/215 20190101; G06F 16/93 20190101;
G06F 16/335 20190101; G06F 16/24542 20190101; G06F 16/2237
20190101 |
International
Class: |
G06F 17/30 20060101
G06F017/30 |
Claims
1. A computer-implemented method, carried out by at least one
server having one or more processors, the method comprising:
receiving a plurality of documents found to be relevant to at least
a portion of a search query, wherein the plurality of documents
includes one or more invalid matching documents; accessing a
representation for each document of the plurality of documents,
wherein the representation includes each term present within each
document; comparing the terms present within each document to one
or more terms associated with the search query; determining that
the one or more invalid matching documents do not include the one
or more terms associated with the search query; and upon
determining that the one or more invalid matching documents do not
include the one or more terms associated with the search query,
removing the one or more invalid matching documents from the
plurality of documents found to be relevant to the at least a
portion of the search query.
2. The method of claim 1, wherein the one or more invalid matching
documents are documents that are not relevant to the at least a
portion of the search but are included in the plurality of
documents found to be relevant to the at least a portion of the
search query.
3. The method of claim 1, wherein the representation includes a
forward index for each document of the plurality of documents.
4. The method of claim 3, wherein the forward index includes each
term included within each document of the plurality of
documents.
5. The method of claim 3, wherein the forward index includes a
portion of terms that is included within each document of the
plurality of documents.
6. The method of claim 1, wherein, prior to removal of the one or
more invalid matching documents, the plurality of documents found
to be relevant to the at least a portion of the search query is
associated with a false positive rate greater than 0%.
7. The method of claim 1, wherein the representation is a data
structure.
8. The method of claim 1, further comprising communicating the
plurality of documents found to be relevant to the at least a
portion of the search query on to a ranker subsequent to removing
the one or more invalid matching documents.
9. One or more computer storage media storing computer-useable
instructions that, when used by one or more computing devices,
cause the one or more computing devices to perform a method, the
method comprising: receiving a first plurality of documents found
to be relevant to at least a portion of a search query, wherein the
first plurality of documents includes one or more invalid matching
documents; receiving a forward index for each document of the first
plurality of documents, wherein the forward index includes one or
more terms included in each document; using the forward index for
each document of the first plurality of documents, identifying one
or more valid matching documents that include one or more terms
associated with the search query; using the forward index for each
document of the first plurality of documents, identifying one or
more invalid matching documents that do not include the one or more
terms associated with the search query; removing the one or more
invalid matching documents from the first plurality of documents to
create a filtered set of one or more documents found to be relevant
to the at least a portion of the search query; and communicating
the filtered set of one or more documents found to be relevant to
the at least a portion of the search query for ranking each
document of the filtered set of one or more documents for the
search query.
10. The media of claim 9, wherein the forward index for each
document is associated with a data structure.
11. The media of claim 9, wherein the first plurality of documents
are received from a preliminary ranker that ranked each document of
the first plurality of documents for the search query.
12. The media of claim 9, wherein the forward index for each
document includes each term included in each document.
13. The media of claim 9, wherein the forward index for each
document includes a portion of terms included in each document.
14. The media of claim 9, wherein a false positive rate of greater
than 0% is associated with the first plurality of documents.
15. A computerized system embodied on one or more computer storage
media having computer-executable instructions provided thereon, the
system comprising: a preliminary ranker component to rank a first
set of documents that are found to be relevant to at least a
portion of a search query by a matcher component, wherein the
initial set of documents includes one or more invalid matching
documents; a match fix-up component to identify when the first set
of documents includes one or more invalid matching documents
utilizing a forward index for each document of the initial set of
documents; and a subsequent ranker to rank a second set of
documents received from the match fix-up component, wherein the
second set of documents includes fewer invalid matching documents
that the first set of documents.
16. The system of claim 15, wherein the match fix-up component
includes the forward index for each document including each term
associated with each document.
17. The system of claim 15, wherein the match fix-up component
includes the forward index for each document including a portion of
terms associated with each document.
18. The system of claim 15, wherein the match fix-up component
includes a data structure associated with the forward index.
19. The system of claim 15, wherein the match fix-up component
removes the one or more invalid matching documents from the first
set of documents.
20. The system of claim 19, wherein, prior to removal of the one or
more invalid matching documents, the first set of documents is
associated with a false positive rate greater than 0%.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit of U.S. Provisional
Application No. 62/183,556, filed Jun. 23, 2015, which is hereby
incorporated herein by reference in its entirety.
BACKGROUND
[0002] The amount of available information and digital content on
the Internet and other electronic sources continues to grow
rapidly. Given the vast amount of information, search engines have
been developed to facilitate searching for electronic documents. In
particular, users or computers may search for information and
documents by submitting search queries, which may include, for
instance, one or more words. After receiving a search query, a
search engine identifies documents that are relevant based on the
search query.
[0003] At a high level, search engines identify search results by
ranking documents' relevance to a search query. Ranking is often
based on a large number of document features. Given a large set of
documents, it's not feasible to rank all documents for a search
query as it would take an unacceptable amount of time. Therefore,
search engines typically employ a pipeline that includes
preliminary operations to remove documents from consideration for a
final ranking process. This pipeline traditionally includes a
matcher that filters out documents that don't have terms from the
search query. The matcher operates using a search index that
includes information gathered by crawling documents or otherwise
analyzing documents to collect information regarding the documents.
Search indexes are often comprised of posting lists (sometimes
called an inverted index) for the various terms found in the
documents. The posting list for a particular term consists of a
list of the documents containing the term. When a search query is
received, the matcher employs the search index to identify
documents containing terms identified from the search query. The
matching documents may then be considered by one or more downstream
processes in the pipeline that further remove documents and
ultimately return a set of ranked search results.
SUMMARY
[0004] This summary is provided to introduce a selection of
concepts in a simplified form that are further described below in
the Detailed Description. This summary is not intended to identify
key features or essential features of the claimed subject matter,
nor is it intended to be used as an aid in determining the scope of
the claimed subject matter.
[0005] The technology described herein provides for a match fix-up
stage to remove invalid matching documents returned from a bit
vector search index. The bit vector search index is a data
structure that uses bit vectors to index information about terms
contained in documents. Each bit vector comprises an array of bits
that stores information for a collection of terms. Each bit
position (or bit) in a bit vector indicates whether one or more
documents contain one or more terms from a collection of terms.
Additionally, a term can be included in multiple bit vectors.
Matching documents for a search query are identified by identifying
bit vectors corresponding to the term(s) from the query and
intersecting the identified bit vectors. The set of matching
documents may include too many matching documents to feasibly send
them all to a final ranker, which may be expensive in the sense of
the amount of processing required for each document. Additionally,
because the bit vector search index provides a probabilistic
approach, some of the matching documents may be invalid matching
documents (i.e., false positives) in the sense that those documents
don't contain terms from the search query. Accordingly, in
accordance with the technology described herein, the search system
employs a match fix-up stage to remove invalid matching documents.
Generally, a representation of each document is used to identify
valid matching documents and invalid matching documents. The
representation may be, for instance, a forward index that stores a
list of terms for each document. Any invalid matching documents are
removed such that they are not considered by the final ranker.
BRIEF DESCRIPTION OF THE DRAWINGS
[0006] Aspects of the technology provided herein are described in
detail below with reference to the attached drawing figures,
wherein:
[0007] FIG. 1 is diagram illustrating bit vector for a single term
in accordance with an aspect of the technology described
herein;
[0008] FIG. 2 is a diagram illustrating a bit vector for a
combination of three terms in accordance with an aspect of the
technology described herein;
[0009] FIG. 3 is a diagram illustrating including terms in multiple
bit vectors in accordance with an aspect of the technology
described herein;
[0010] FIG. 4A-4C are diagrams illustrating intersecting bit
vectors to identify documents that include a term in accordance
with an aspect of the technology described herein;
[0011] FIG. 5 is a diagram illustrating bit vectors with different
numbers of documents per bit in accordance with an aspect of the
technology described herein;
[0012] FIG. 6 is a flow diagram illustrating a method for
generating a search index using bit vectors in accordance with an
aspect of the technology described herein;
[0013] FIG. 7 is a diagram illustrating a simplified search index
700 using bit vectors in accordance with an aspect of the
technology described herein;
[0014] FIG. 8 is a flow diagram illustrating a method for a matcher
to identify documents that match terms from a search query in
accordance with an aspect of the technology described herein;
[0015] FIG. 9 is a flow diagram illustrating a method for
intersecting bit vectors using short bit vectors first in
accordance with an aspect of the technology described herein;
[0016] FIG. 10 is a diagram illustrating an example of bit vectors
available for terms from a search query in accordance with an
aspect of the technology described herein;
[0017] FIG. 11 is a diagram illustrating ordering the bit vectors
for intersection in accordance with an aspect of the technology
described herein;
[0018] FIG. 12 is a diagram illustrating forming a query plan in
accordance with an aspect of the technology described herein;
[0019] FIG. 13 is a diagram illustrating a tree for a query plan in
which each block corresponds to a bit vector in accordance with an
aspect of the technology described herein;
[0020] FIGS. 14-17 are diagrams illustrating intersections of bit
vectors in accordance with the tree for the query plan of FIG. 13
in accordance with an aspect of the technology described
herein;
[0021] FIG. 18 is a flow diagram illustrating a method for a
matcher to generate a matcher plan that provides an efficient order
for intersecting bit vectors in accordance with an aspect of the
technology described herein;
[0022] FIG. 19 is a flow diagram illustrating a method for matching
documents using strengthening rows in accordance with an aspect of
the technology described herein;
[0023] FIGS. 20A-20B are diagrams illustrating an example of using
bit vectors for a phrase in accordance with an aspect of the
technology described herein;
[0024] FIG. 21 is a diagram providing an example of a long
document;
[0025] FIG. 22 is a flow diagram illustrating a method for
generating shards for a search index using bit vectors in
accordance with an aspect of the technology described herein;
[0026] FIG. 23 is a flow diagram illustrating a method for
performing a search using multiple shards in accordance with an
aspect of the technology described herein;
[0027] FIG. 24 is a flow diagram illustrating a method for
generating a data structure, such as a band table, mapping term
characteristics to bit vector configurations in accordance with an
aspect of the technology described herein;
[0028] FIG. 25 is a flow diagram illustrating a method for
determining bit vector storage locations using explicit mappings
and ad hoc information in accordance with an aspect of the
technology described herein;
[0029] FIG. 26 is a flow diagram illustrating a method for row
trimming/augmentation for a search query in accordance with an
aspect of the technology described herein;
[0030] FIG. 27 is a flow diagram illustrating another method for
row trimming/augmentation for a search query in accordance with an
aspect of the technology described herein;
[0031] FIG. 28 is a flow diagram illustrating a method for adding a
document to a bit vector-based search index in accordance with an
aspect of the technology described herein;
[0032] FIG. 29 is diagram illustrating a simplified search index
with a collection of bit vectors of varying length with a "column"
for a document identified.
[0033] FIG. 30 is a flow diagram illustrating a method for removing
a document from a bit vector search index in accordance with an
aspect of the technology described herein;
[0034] FIGS. 31A-D are diagrams illustrating removing a document
from a bit vector search index in accordance with an aspect of the
technology described herein;
[0035] FIGS. 32A and 32B are diagrams illustrating adding a
document to an array;
[0036] FIGS. 33A-33C are further diagrams illustrating adding
documents to an array;
[0037] FIGS. 34A and 34B are diagrams illustrating copying
documents to a larger array and starting a new array,
respectively;
[0038] FIGS. 35A-35H are diagrams illustrating writing documents to
an array and copying documents from array to array;
[0039] FIG. 36 is a diagram illustrating storing different arrays
on different types of storage;
[0040] FIG. 37 is a flow diagram illustrating a method for using an
accumulation buffer to index documents in a bit vector search index
in accordance with an aspect of the technology described
herein;
[0041] FIG. 38 is a block diagram illustrating an exemplary system
providing preliminary ranking in accordance with an aspect of the
technology described herein;
[0042] FIG. 39 is a flow diagram illustrating a method for scoring
a plurality of documents based on relevancy to a search query in
accordance with an aspect of the technology described herein;
[0043] FIG. 40 is a flow diagram illustrating a method for scoring
a plurality of documents based on relevance to a search query in
accordance with another aspect of the technology described
herein;
[0044] FIG. 41 is a flow diagram illustrating a method for adding
data for a term to slots of a score table in accordance with an
aspect of the technology described herein;
[0045] FIG. 42 is a flow diagram illustrating a method for
employing match fix-up to remove invalid matching documents
downstream from a probabilistic matcher in accordance with an
aspect of the technology described herein;
[0046] FIG. 43 is a flow diagram illustrating another method for
employing match fix-up to remove invalid matching documents
downstream from a probabilistic matcher in accordance with an
aspect of the technology described herein;
[0047] FIG. 44 is a block diagram illustrating an exemplary search
system in which aspects of the technology described herein may be
employed; and
[0048] FIG. 45 is a block diagram of an exemplary computing
environment suitable for use in implementing aspects of the
technology described herein.
DETAILED DESCRIPTION
[0049] The subject matter of aspects of the technology provided
herein is described with specificity herein to meet statutory
requirements. However, the description itself is not intended to
limit the scope of this patent. Rather, the inventors have
contemplated that the claimed subject matter might also be embodied
in other ways, to include different steps or combinations of steps
similar to the ones described in this document, in conjunction with
other present or future technologies. Moreover, although the terms
"step" and/or "block" may be used herein to connote different
elements of methods employed, the terms should not be interpreted
as implying any particular order among or between various steps
herein disclosed unless and except when the order of individual
steps is explicitly described.
[0050] Each method described herein may comprise a computing
process performed using any combination of hardware, firmware,
and/or software. For instance, various functions may be carried out
by a processor executing instructions stored in memory. The methods
may also be embodied as computer-usable instructions stored on
computer storage media. The methods may be provided by a standalone
application, a service or hosted service (standalone or in
combination with another hosted service), or a plug-in to another
product, to name a few.
[0051] Alternatively, or in addition, the functionality described
herein can be performed, at least in part, by one or more hardware
logic components. For example, and without limitation illustrative
types of hardware logic components that can be used include
Field-programmable Gate Arrays (FPGAs), Application-specific
Integrated Circuit (ASICs), Application-specific Standard Products
(ASSPs), System-on-a-chip systems (SOCs), Complex Programmable
Logic Devices (CPLDs), etc.
[0052] A number of metrics may be considered when evaluating the
design of a search system. One metric is storage consumption used
by the search system to index information regarding a corpus of
documents. This metric may be a measure of the number of documents
that can be indexed on each machine in the search system ("D").
Another metric is processing speed for search queries. This metric
may be a measure of the number of queries per second processed by
the search system ("Q"). Another consideration in the design of a
search system is that it should always be available even though the
search index needs to be periodically updated to index information
about document changes and new documents. In some designs, search
systems are updated by taking banks of index servers down to update
them while leaving other banks running such that all banks are
updated over time. With the continual increase of available
documents on the Internet, the time to update the search index
continues to rise to a point where current designs may become
unfeasible. Finally, another design goal for a search system may to
quickly update the search index with new documents as they become
available. This is particularly desirable for indexing information
such as news or social feeds in which users expect to see
information in near real-time as the information becomes
available.
[0053] When considering the above design metrics, the traditional
use of posting lists (or inverted index) presents a number of
drawbacks that impact both how many documents can be stored on
machines in the search system (D) and the processing speed of
queries (Q). Posting lists are kept sorted so that a join (or
intersection) of two posting lists can be performed efficiently.
However, re-sorting posting lists makes instant updating of
information impractical, because a large amount of data must be
rebuilt for every update. Thus, posting lists often require batch
updating to amortize the sort costs over a larger number of
updates. To speed up query processing, a number of complexities
have been added to posting lists, such as skip lists that provide a
way of skipping over documents when searching the posting lists for
matching documents that contain search query terms. Additionally,
because posting lists are typically sorted by document, if a new
document is added, it may have to be inserted somewhere in the
middle of the posting list. Given these complexities, posting lists
may not allow for the quick insertion of new documents or document
changes but may instead require the posting lists to be rewritten.
Even if the design does facilitate insertion of new documents or
document changes, it may be very complicated to insert it because
of skip lists and/or other complexities added to the posting lists
to facilitate query processing. As a result, the time to update a
search index for a large corpus of documents, such as documents
available via the Internet, may continue to increase to a point
where it cripples the availability of the search system.
Additionally, these issues negatively impact the ability of the
search system to provide real-time search results for newly
available information (e.g., news, social feeds, etc.).
[0054] Aspects of the technology described herein employ a number
of techniques to produce large increases in efficiency over
existing search systems (e.g., 2-3.times. over all search engines
and 10.times. over search engines with instant update). This
includes replacing posting lists with data structures that attempt
to maximize the information density across an I/O channel. For
instance, in today's Xeon computers, the limiting channel might be
the path from memory to the CPU, where the memory could be, for
instance, double data rate random-access memory (DDR RAM or DDR),
solid-state drive (SSD), or hard disk drive (HDD). The organization
of data is mostly optimized by entropy, in order to approach the
theoretical maximum information density. Aspects of the technology
described herein employ probabilistic approaches that allow false
positive results to occur during the matching process. In other
words, the matcher may return documents that don't contain terms
from a search query. This is in contrast to posting lists, which
are exact--the matcher will only return documents that contain
terms from a search query. However, the resulting efficiency
improvements are so profound with the techniques employed by
various configurations described herein that, even when accounting
for the cost to remove the false positives in a later stage, the
total cost for matching is significantly reduced when compared to
systems that utilize posting lists. Additionally, while the matcher
may return false positives, it will not remove documents that are
true matches (except when the NOT operator is used).
[0055] FIG. 44 provides a block diagram showing an exemplary search
system 4400 providing an overview of features described herein. It
should be understood that this and other arrangements described
herein are set forth only as examples. Other arrangements and
elements (e.g., machines, interfaces, functions, orders, and
groupings of functions, etc.) can be used in addition to or instead
of those shown, and some elements may be omitted altogether.
Further, many of the elements described herein are functional
entities that may be implemented as discrete or distributed
components or in conjunction with other components, and in any
suitable combination and location. Various functions described
herein as being performed by one or more entities may be carried
out by hardware, firmware, and/or software. For instance, various
functions may be carried out by a processor executing instructions
stored in memory. Although FIG. 44 shows a search system 4400 with
a number of different features, it should be understood that search
systems may employ any of the features independent of other
features discussed herein.
[0056] As shown in FIG. 44, the search system 4400 employs a bit
vector search index 4410 instead of a search index using postings
lists. The bit vector search index 4410 uses a number of bit
vectors to represent indexed documents. As will be described in
more detail below, a bit vector is an array of bits that stores
information for a collection of terms. Each bit position (or bit)
in a bit vector corresponds to an assertion of whether one or more
documents contain one or more terms from a collection of terms. As
used herein, a "document" refers to any electronic content item for
which information may be indexed by a search system. An electronic
content item is not limited to text and could include, for
instance, images, audio, video, geographic data, etc. As used
herein, a "term" corresponds to any assertion about a document,
including the assertion that the document contains one or more
specific words. In some instances, a term may be a single word;
while in other instances, a term may be a multiword phrase. A
"word" refers to any number of symbols (e.g., letters, numbers,
punctuation, etc.) or any binary data (such as hash, index, id,
etc.). In some configurations, a term may be a "metaword" that
encodes other types of assertions beyond a word or collection of
words. For instance, a term may correspond to the assertion that a
document is written in French.
[0057] Because a bit vector may correspond to a collection of
terms, the bit vector includes noise in the sense that it is
unknown from a set bit in a single bit vector which of those terms
is contained in a document corresponding to the set bit. To address
this, a term may be included in multiple bit vectors. To identify
documents containing a given term, bit vectors corresponding to
that term are identified and intersected. Documents containing the
term are identified as ones corresponding to a certain bit that is
set in each of the bit vectors for the term. It should be noted
that the majority of this description discusses intersection of bit
vectors. However, configurations may also employ unions and
negations, and as such, where an intersection is mentioned, a union
or negation could be performed instead.
[0058] The bit vectors may include both long and short bit vectors.
A long bit vector is a bit vector in which each bit corresponds to
a single document. Therefore, a bit in a long bit vector indicates
whether a document corresponding to that bit contains one or more
of the terms corresponding to that bit vector. A short bit vector
is a bit vector in which each bit corresponds to two or more
documents. Therefore, a bit in a short bit vector indicates whether
any of the two or more documents corresponding to that bit contains
one or more of the terms corresponding to that bit vector. The
search index may store varying lengths of short bit vectors (e.g.,
two documents per bit, four documents per bit, eight documents per
bit, etc.).
[0059] Using a bit-vector based search index provides a number of
benefits over posting lists. For instance, by using sequences of
bits, the approach creates very high efficiencies by avoiding the
complexities of posting lists including the need to sort documents
and the use of skip lists. This allows, among other things, instant
or near-instant update of the search index, preventing long
downtimes to update the search index and facilitating the real-time
or near real-time addition of new/changed documents (e.g., for
news, social feeds, etc.). Additionally, the design of the system
is configurable to meet Q and D design goals. For instance, the
approach may provide extremely high Q without sacrificing D (i.e.,
high Q while D is comparable to existing systems). As another
example, the approach may provide high D without sacrificing Q
(i.e., high D while Q is comparable to existing systems).
[0060] In some configurations, the bit vector-based search index is
divided into different shards 4412 as represented in FIG. 44. Each
shard indexes a different collection of documents corresponding to
a different range of document length (e.g., the number of unique
terms indexed for a document). For instance, a first shard may
index documents with 0-100 terms, a second shard could index
documents with 101-200 terms, a third shard could index documents
with 201-300 terms, etc. This addresses the issue that efficiencies
are lost when documents of greatly varying length are stored
together. For instance, if a very long document (i.e., many terms
are indexed) is stored with a very short document (i.e., very few
terms are indexed), the column for the long document will have many
bits set, while the column for the short document will have very
few bits set. As used herein, a "column" refers to the bits in each
bit vector that corresponds to a given document or group of
documents. This makes it difficult to maintain a uniform bit
density (i.e., the percentage of bits set to "1") in the bit
vectors. By grouping similar length documents in shards, the
distribution of terms may be configured in a manner to better
control the bit density.
[0061] The distribution of terms in a bit vector-based search index
may be achieved in some configurations by assigning different bit
vector configurations to different terms. A bit vector
configuration for a term represents the number and length of bit
vectors used for a term and may also specify the type of storage
(e.g., DDR, SSD, HDD, etc.) for each bit vector. In accordance with
some aspects of the technology described herein, terms may be
grouped into bands based on term characteristics and each band may
be assigned a particular bit vector configuration. This avoids the
complexities of assigning a bit vector configuration on a per-term
basis and avoids the inefficiencies of a one-size-fits-all solution
using a single bit vector configuration for all terms. The mapping
of term characteristics to bit vector configurations may be stored
in a data structure by the search system 4400, such as in the band
table 4414 shown in FIG. 44.
[0062] Some configurations address the identification of storage
locations of bit vectors for terms. For instance, bit vector
storage locations are identified when generating and updating the
search index. Additionally, bit vector storage locations are
identified when retrieving bit vectors for the purpose of
identifying matching documents for a search query. In accordance
with some aspects of the technology described herein, a hybrid
approach for identifying bit vector storage locations is employed.
An explicit mapping is provided for some terms. This may include,
for instance, terms that occur most frequently in search queries
and/or documents. The explicit mappings identify specific bit
vector storage locations for each term. The explicit mappings may
be stored in a data structure by the search system 4400, such as
the term table 4416 shown in FIG. 44. For other terms, an ad hoc
approach is employed. In particular, mapping algorithms may be
provided for bands of terms that correspond to particular term
characteristics. The mapping algorithms for each band may be
employed for deriving the bit vector storage locations for terms
that have term characteristics assigned to each corresponding band.
Each mapping algorithm may determine storage locations, for
instance, as a function of the hash of a term. The correspondence
of mapping algorithms to term characteristics may be stored in a
data structure by the search system 4400, such as in the band table
4414 shown in FIG. 44.
[0063] The band table 4414 and term table 4416 may be used by the
search system 4400 at both index generation time and query time. As
used herein, "index generation time" refers to processes to index
information regarding documents in a search index. This includes
initially generating the search index and incrementally updating
the search index over time by adding/updating/removing documents.
As used herein, "query time" refers to processing search queries to
return search results. The band table 4414 and term table 4416 may
be used by an indexer 4418 at index generation time to index
information about documents in the bit vectors of the bit vector
search index 4410. In particular, the band table 4414 and term
table 4416 may be used to identify bit vector configurations for
terms and identify bit vector locations for terms when adding
document information to the bit vector search index 4410. At query
time, the matcher 4404 may employ the band table 4414 and/or term
table 4416 to identify bit vector locations for terms identified
from a received search query.
[0064] The indexer 4418 may be operable to add and/or remove
documents from the bit vector search index 4410. Adding documents
to the bit vector search index 4410 may simply entail identifying a
"column" for the document (i.e., a bit in each bit vector
corresponding to the document) and setting bits in bit vectors
corresponding to that column based on the presence of terms in the
document. In some instances, faster storage devices (e.g., DDR RAM)
may be employed to store bit vectors and documents may be indexed
one at a time. In other instances, slower storage devices (e.g.,
SSD; HDD) may present some inefficiencies when writing bit vectors.
Accordingly, some configurations employ what is referred to herein
as "accumulation buffers" to index documents to slower storage
devices to offset inefficiencies. Generally, documents may be
initially indexed in bit vectors in an accumulation buffer. Once a
threshold is met (e.g., time-based; document-based), information is
transferred from the accumulation buffer to another storage device.
Any number and size of accumulation buffers may be employed to
index documents to a final storage device depending on design
goals.
[0065] FIG. 44 illustrates a multistage approach to providing
ranked searched results 4428 for a search query 4402. When the
search query 4402 is received by the search system 4400, terms are
identified based on the search query 4402. The terms may be terms
exactly as included in the search query and/or terms derived based
on the terms in the search query 4402. The matcher 4404 operates to
identify a set of matching documents 4420 based on the terms from
the search query 4402. The matcher 4404 includes a bit vector
selection component 4406 that generally operates to select bit
vectors for the terms from the bit vector search index 4410. The
matcher 4406 also includes a bit vector processing component 4408
that operates to intersect (or perform a union or exclusion (e.g.,
not) on) the selected bit vectors in order to identify the set of
matching documents 4420.
[0066] A number of techniques may be employed by the bit vector
selection component 4406 in selecting bit vectors for intersection
in order to control the matching documents returned. Some aspects
of the technology described herein may employ what is referred to
herein as "strengthening row" bit vectors in instances in which too
many matching documents may be returned. A "strengthening row" bit
vector is a bit vector that is added in addition to the term bit
vectors for intersection in order to reduce the number of matching
documents. As an example, a strengthening row bit vector may be
based on static rank of documents. In particular, a bit vector may
have bits set for documents with the highest static rank (e.g., the
top 10% of documents based on static rank). Adding such a static
rank bit vector would limit the matching documents to documents
with the highest static rank that match the terms from the search
query 4402. Another strengthening row bit vector that may be used
is a bit vector that identifies terms in non-body locations (e.g.,
title or anchor text) in documents (as opposed to any location in
the documents).
[0067] Another technique that may be used by the bit vector
selection component 4406 in selecting bit vectors is referred to
herein as row trimming/augmentation. A number of bit vectors are
typically available for each term from a received search query, and
the bit vectors may be stored in different types of storage (e.g.,
DDR, SSD, HDD, etc.). The bit vector selection component 4406 may
decide which of the available bit vectors for the terms from the
search query 4402 to select for intersection. The selection may be
based on some relevance metric, an estimate of the number of
matching documents expected to be returned, the type of storage at
which each bit vector is located, and other considerations.
Controlling the selection of which available bit vectors for
intersection, the relevance of the matching documents (e.g., number
of false positives) and the processing speed may be adjusted based
on design goals for the search system 4400.
[0068] The set of matching documents 4420 returned by the matcher
4404 may include too many matching documents to feasibly send them
all to a final ranker 4426, which may be expensive in the sense of
the amount of processing required for each document. Additionally,
because the bit vector search index provides a probabilistic
approach, some of the matching documents 4420 may be invalid
matching documents (i.e., false positives) in the sense that those
documents don't contain terms from the search query. Accordingly,
the search system 4400 may employ one or more stages between the
matcher 4404 and the final ranker 4426 to remove matching documents
from consideration before reaching the final ranker 4426.
[0069] One or more preliminary rankers, such as the preliminary
ranker 4422, may provide less expensive ranking of documents to
more quickly remove some documents from consideration. Typically,
preliminary rankers may employ information from posting lists.
Because the search system 4400 does not employ posting lists, other
approaches may be employed. In accordance with some aspects of the
technology described herein, score tables 4430 may be used by the
preliminary ranker for scoring matching documents based on their
relevance to a search query. A score table for a document stores
pre-computed data used to derive a frequency of terms and other
information in the document. Accordingly, the preliminary ranker
4422 may employ the score table for each matching document and the
terms from the search query 4402 to determine a score for each
matching document. The lowest scoring documents may then be removed
from further consideration.
[0070] The search system 4400 may also employ a match fix-up stage
to remove invalid matching documents. Generally, a match fix-up
component 4424 may employ a representation of each document to
identify valid matching documents and invalid matching documents.
The representation may be, for instance, a forward index that
stores a list of terms for each document. Any invalid matching
documents may be removed by the match fix-up component 4424 such
that they are not considered by the final ranker.
Search Index Using Bit Vectors
[0071] The search index in aspects of the technology described
herein employs bit vectors instead of posting lists traditionally
used by search indexes. A bit vector comprises an array of bits
(i.e., ones and zeroes). In its simplest form, a bit vector may
correspond to a particular term and each bit corresponds to
particular document. A bit being set for a document indicates the
document contains the term. Conversely, a bit not being set for a
document indicates the document does not contain the term.
[0072] FIG. 1 conceptually illustrates a bit vector 100 for a term
A. Each of the 24 blocks shown in FIG. 1 corresponds to a bit in
the array, each bit corresponding to a different document.
Accordingly, the bit vector 100 encodes information regarding
whether each of 24 documents contains the term A. In the present
example, the blocks 102, 104, 106 marked with the letter A
represent bits that have been set, thereby indicating the documents
corresponding to those bits contain the term A. Therefore, the bit
vector 100 identifies three documents, in the set of 24 documents,
that contain the term A. Conceptually, the bit vector 100 is shown
as a row, and the terms "bit vector" and "row" may be used
interchangeably herein.
[0073] In practice for a search engine that indexes a large
collection of documents, using a bit vector to represent a single
term would be impractical. In particular, the bit vector would
include a very large number of bits corresponding to the large
collection of documents, and the entire array of bits would need to
be scanned to find bits that have been set. For many terms, the bit
vector would be very sparse (i.e., only a small percentage of the
bits are set) since only a small fraction of the indexed documents
contain the term. As a result, the bit vector would not present a
compact solution, and as a result, it would take a large of amount
of storage to store the index and processing a search query would
take an unacceptable amount of time.
[0074] To address this issue of sparseness, a technique referred to
herein as "row sharing" is used in which multiple terms are
included in a bit vector to increase the bit density of the bit
vector (i.e., the percentage of bits set for the bit vector).
Conceptually, this may be done by taking a bit vector for each of
the terms and creating a union of those bit vectors. For instance,
FIG. 2 illustrates a bit vector 202 for the term A, a bit vector
204 for the term B, and a bit vector 206 for the term C. A bit
vector 208 that contains the terms A, B, and C could be generated
as a union of the bit vectors 202, 204, and 206. As can be seen
from FIG. 2, each of the bit vectors 202, 204, 206 only have three
bits set and are sparse compared to the bit vector 208, which has
nine bits set. As such, combining the three terms in a single bit
vector increases the bit density. Instead of having three bits set
as in each of the bit vectors 202, 204, and 206, the bit vector 208
has nine bits set.
[0075] One consequence of including multiple terms in a bit vector
is that a single bit vector does not provide enough information to
determine which term a document contains based on a bit being set
in the bit vector for that document. In the example of FIG. 2 in
which the bit vector includes terms A, B, and C, a bit being set
for a document indicates the document contains A, or B, or C, or
some combination of those terms. However, it can't be determined
from the single bit in the bit vector which of the terms the
document contains. Therefore, a mechanism is needed to determine
which term the document contains.
[0076] Aspects of the technology described herein address this
issue created from having multiple terms in a bit vector by
including a term in multiple bit vectors with different terms. This
technique is referred to herein as "term copies." FIG. 3
illustrates the concept of term copies. As shown in FIG. 3, three
bit vectors 302, 304, and 306 each include the term A. However, the
other included terms differ among the three bit vectors 302, 304,
and 306. In particular, in addition to term A, bit vector 302
includes terms B and C, bit vector 304 includes terms D and E, and
bit vector 306 includes terms F and G.
[0077] The identification of which documents contain a particular
term may be determined by a technique referred to herein as "row
intersections" in which bit vectors that contain a term are
intersected. Intersecting the bit vectors removes noise (i.e., bits
set based on the presence of other terms) to identify which
documents contain the desired term. Continuing the example of FIG.
3, FIG. 4A is another representation of the term A being included
with other terms in three bit vectors 402, 404, and 406. As such,
there are three bit vectors with a correlated signal (i.e., the
presence of the term A) and uncorrelated noise (the presence of
other terms--B, C, D, E, F, G). In the example of FIG. 4A, the
noise bits are shown with hatching.
[0078] Some of the noise may be removed by intersecting bit vector
404 and bit vector 406. The result of the intersection is a bit
vector 408 shown in FIG. 4B with bits set only in locations in
which bits were set for both bit vectors 404 and 406. This includes
the fourth, seventh, eleventh, sixteenth, and eighteenth positions.
Intersecting this bit vector 408 with the bit vector 402 results in
a bit vector 410 shown in FIG. 4C that includes bits set only in
locations in which bits were set for both bit vectors 408 and 402.
As represented in FIG. 4C, the bit vector 410 includes bits set for
the fourth, eleventh, and eighteenth positions. These correspond to
the documents that contain the term A. Accordingly, by identifying
the bit vectors that include the term A and intersecting those bit
vectors, the documents containing term A are identified. While
FIGS. 4A-4C provide a simplified example in which only documents
containing a particular term are identified (i.e., no false
positives), in practice, row intersections may be designed to
exponentially reduce noise (i.e., false positives), although some
false positives may be present following the row intersections.
[0079] If a large number of documents are indexed and each bit of
the bit vectors corresponds to a single document, the bit vectors
will be long arrays and intersecting the bit vectors may be overly
time-consuming. To address this issue, bit vectors may be employed
that include multiple documents per bit. In a bit vector with
multiple documents per bit, a bit is set if one or more of the
documents sharing the bit contain one of the terms for the bit
vector.
[0080] FIG. 5 illustrates the concept of bit vectors with different
numbers of documents per bit. Initially, the bit vector 502
illustrates the previously discussed bit vectors in which each bit
corresponds to a single document. A bit vector, such as the bit
vector 502, in which there is one document per bit is referred to
herein as a "long row" bit vector. As shown in FIG. 5, the bit
vector 502 includes 32 bits corresponding to 32 documents. Bits
have been set for the fourth, nineteenth, twenty-fifth, and
twenty-seventh documents.
[0081] The bit vectors 504, 506, 508 are referred to herein as
"short row" bit vectors because each bit includes two or more
documents, thereby providing shorter arrays of bits. The bit vector
504 includes two documents per bit (16 total bits), the bit vector
506 includes four documents per bit (eight total bits), and the bit
vector 508 includes eight documents per bit (four total bits). Each
of the bit vectors 504, 506, 508 shown in FIG. 5 corresponds to the
terms and documents from the bit vector 502. Each bit in a shorter
bit vector corresponds to multiple bits from a longer bit vector.
For instance, for the bit vector 504 (2 documents per bit), the
first bit (bit position 0) corresponds to the bit positions 0 and
16 in the bit vector 502, and the second bit (bit position 1)
corresponds to bit positions 1 and 17 in the bit vector 502, etc.
For the bit vector 506 (4 documents per bit), the first bit (bit
position 0) corresponds to bit positions 0, 8, 16, and 24 in the
bit vector 502, and the second bit (bit position 1) corresponds to
bit positions 1, 9, 17, and 25 in the bit vector 502, etc. For the
bit vector 508 (8 documents per bit), the first bit (bit position
1) corresponds to bit positions 0, 4, 8, 12, 16, 20, 24, and 28 in
the bit vector 502, and the second bit (bit position 0) corresponds
to bit positions 1, 5, 9, 13, 17, 21, 25, and 29 in the bit vector
502, etc.
[0082] The bits in each of the bit vectors 504, 506, 508 are set if
one of the corresponding bits are set in the bit vector 502. The
following are examples to illustrate this. Because neither bit 0
nor bit 16 is set in the bit vector 502, bit 0 in the bit vector
504 is not set. However, because at least one of bits 2 and 18 is
set in the bit vector 502 (i.e., bit 18 is set), bit 2 is set in
the bit vector 504. In the bit vector 506, bit 3 is set because at
least one of bits 3, 11, 19, and 27 in the bit vector 502 is set
(i.e., bit 3 is set). Bit 2 in the bit vector 508 is set because at
least one of bits 2, 6, 10, 14, 18, 22, 26, and 30 in the bit
vector 502 is set (i.e., bits 18 and 26 are set).
[0083] As used herein, short row bit vectors may be referred to as
"rank-n" bit vectors if the bit vectors have 2.sup.n document per
bit. For example, the bit vector 502 may be referred to as a rank-0
bit vector (because it contains 2.sup.0=1 document per bit), the
bit vector 504 may be referred to as a rank-1 bit vector (because
it contains 2.sup.1=2 documents per bit), the bit vector 506 may be
referred to as a rank-2 bit vector (because it contains 2.sup.2=4
documents per bit), and the bit vector 508 may be referred to as a
rank-3 bit vector (because it contains 2.sup.3=8 documents per
bit).
[0084] Turning now to FIG. 6, a flow diagram is provided that
illustrates a method 600 for generating a search index using bit
vectors. The method 600 may be performed at least in part, for
instance, using the indexer 4418 of FIG. 44. As shown at block 602,
terms are assigned to bit vectors. As discussed above, each term
may be assigned to multiple bit vectors. Additionally, multiple
terms are assigned to at least some of the bit vectors; although
some bit vectors may have only a single term assigned. Some of the
bit vectors are established as long row bit vectors with each bit
corresponding to a single document and some of the bit vectors are
established as short row bit vectors with each bit corresponding to
multiple documents.
[0085] Documents are assigned to bit positions in the bit vectors,
as shown at block 604. In long row bit vectors, each document
corresponds to a single bit position in the bit vectors. In short
row bit vectors, multiple documents correspond to each bit
position. A document is assigned to a bit position in a short row
bit vector corresponding to the bit position assigned to the
document in long row bit vectors. It should be understood that any
of a variety of different approaches may be employed to define bit
correspondences between ranks. In some configurations, bit
correspondences between ranks are based on the following
equations:
Bit i of quad word j in a row of rank r maps to bit i of quad word
j 2 in a row of rank r + 1. Equation 1 Bit i of quad word j in a
row of rank r corresponds to bit i in quad words 2 j and 2 j + 1 in
a row of rank r - 1. Equation 2 ##EQU00001##
[0086] As shown at block 606, bits are set in the bit vectors based
on the presence of terms in the documents. For each document, the
terms contained in the document are identified, bit vectors
corresponding to each term are identified, and the bits assigned to
the document in each of those bit vectors are identified and set.
In some instances, a bit may have already been set when processing
a different term and/or document.
[0087] FIG. 7 illustrates an example of a very simple search index
700 using bit vectors. The search index 700 stores 16 bit vectors,
each bit vector comprising an array of bits. The bit vectors
include four long row bit vectors 702 with each bit corresponding
to a single document. As can be seen in FIG. 7, each long row bit
vector 702 includes 32 bits such that the search index 700 indexes
information for 32 documents. The search index 700 also stores a
number of short row bit vectors. In particular, the search index
700 stores four rank-1 bit vectors 704 (i.e., two documents per
bit), four rank-2 bit vectors 706 (i.e., four documents per bit),
and four rank-3 bit vectors 708 (i.e., eight documents per
bit).
[0088] Each bit vector may correspond to multiple terms.
Additionally, each term may be included in at least one long row
bit vector and at least one short row bit vector. Accordingly, each
bit in a long row bit vector represents whether a particular
document contains at least one term from a set of terms
corresponding to the bit vector. Each bit in a short row bit vector
represents whether at least one of a set of documents contains at
least one term from a set of terms corresponding to the bit vector.
As can be understood from FIG. 7 and the above discussion, each bit
vector includes bits that are consecutive in storage to represent
which documents contain one or more of the terms represented by the
bit vector. In contrast, bits for a document indicating which terms
the document contains are spread out amongst bit vectors and
therefore are non-consecutive in storage. This approach supports
serving search queries since the bits for a bit vector
corresponding to a term from a query are consecutive in storage and
therefore may be quickly retrieved.
Term Distribution in Search Index
[0089] The distribution of terms in a search index using bit
vectors is configurable based on the desired design optimization of
the search index, including storage requirements (e.g., the number
of documents that can be stored on each machine) and processing
speed (e.g., the number of queries that can be performed per
second). Generally, it is desirable to reduce storage requirements
and increase processing speed. However, as discussed in further
detail below, there are tradeoffs in storage requirements and
processing speed with various term distribution aspects.
[0090] One term distribution aspect involves the number of terms to
include in each bit vector. More terms per bit vector allows for
more documents to be stored per machine, thereby reducing overall
storage requirements. However, more terms per bit vector generally
increases the noise, reducing processing speed since additional
processing is required to remove the noise when performing search
queries.
[0091] Another term distribution aspect is the number of copies of
each term to include in the search index (i.e., how many bit
vectors contain information about a specific term). Noise created
by including multiple terms in a bit vector can later be removed if
terms are stored in multiple bit vectors. However, increasing the
number of bit vectors including a particular term increases storage
requirements. Additionally, increasing the number of bits vectors
including a particular term reduces processing speed since more
intersections must be performed.
[0092] A further term distribution design aspect is the mixture of
long row bit vectors (i.e., one document per bit) versus short row
bit vector (i.e., multiple documents per bit). Shorter bit vectors
increase processing speed since there is less memory to scan when
performing row intersections. However, shorter bit vectors increase
noise because, for a given set bit, it is unknown which document
actually contains a term. The mixture of long and short row bit
vectors doesn't impact storage requirements.
[0093] The following provides exemplary rules of thumb for term
distribution in accordance with one implementation. In accordance
with the present example, if a 10% bit density and a 10:1 ratio of
signal to noise is desired, the number of intersections is equal to
the inverse document frequency (IDF) for a term (except for a term
with an IDF of 1, in which the signal to noise ratio is 1.0). The
IDF of a term may be determined by taking the logarithm of the
total number of documents divided by the number of documents
containing the term. For instance, a term appearing once in every
10,000 documents has an IDF of four. When bit vectors that have 10%
of bits set are intersected together, the bits are relatively close
together--usually/often in the same byte. However, by the time four
of those rows have been intersected together, the bits are far
enough apart that they are farther apart than a processor/CPU cache
line (i.e., 64 bytes=512 bits; although this may be different in
different CPUs). As a result, for a certain number of intersections
(e.g. 4 in this example), the entire bit vector is scanned and
every single bit is accessed in order to perform intersections.
However, after enough intersections are completed, the bits are far
enough apart that probing can be done into random locations in the
remaining bit vectors to be interested (i.e., it is not necessary
to scan through all cache lines, although probing a single bit in a
cache line will still cause the entire cache line to be read from
memory. A certain minimum number of bit vectors must be intersected
in their entirety, but after intersecting that certain number of
bit vectors, the cost of additional intersections drops
dramatically (to one cache miss per set bit vs cache misses
required to read an entire row). One take away from this is that
arbitrarily long sequences of intersections have about the same
cost to process as simple queries. This is because the cost for
each query is dominated by the first N intersections in which all
bits from bit vectors are accessed. After those N intersections,
the number of additional bit vectors that are intersected doesn't
add much cost because few cache lines are read in those rows. Since
these first N intersections requiring reading the bit vectors in
their entirety and terms may be stored in a combination of long and
short bit vectors, it may be desirable to use the shortest bit
vectors possible for those first N intersections since it costs
less to scan a shorter bit vector. Therefore, the design of the
search index may maximize the number of short bit vectors in which
a term is included. However, at least one long bit vector (i.e.,
rank-0) bit vector may be used to get down to the resolution of a
single document.
[0094] By way of example to illustrate, suppose the term
"snoqualmie" has an IDF of 4 (meaning it appears once in about
every 10,000 documents). 1000 terms with an IDF of 4, like the term
"snoqualmie," could be combined into a single bit vector to get a
10% bit density. To drive the false positive rate to 10% of the
signal, 4 intersections of 5 bit vectors would be required to drive
the noise down to 1/100,000. Therefore, the term "snoqualmie" could
be stored in 5 rows. Since short rows are faster to scan, but at
least one long row is needed, the term would likely be mapped to 4
short bit vectors and one long bit vector.
Matcher Algorithm
[0095] When a search engine employing a bit vector-based search
index receives search queries, a matcher may employ bit vectors to
identify a set of documents that contain terms from the search
queries. In a common scenario, bit vectors that correspond to terms
contained in and/or derived from the search queries are intersected
to identify matching documents (unions and negations are also
possible, but may be less common). A matcher plan or query plan
(used interchangeably herein) is developed based on the search
query in order to determine how to identify bit vectors for
intersection and/or determining the order in which to perform bit
vectors intersections, as will be described in more detail below.
The matching documents identified from the matching process may
then be analyzed in one or more subsequent processes that rank the
matching documents.
[0096] Turning to FIG. 8, a flow diagram is provided that
illustrates a method 800 for a matcher to identify documents that
match terms from a search query. The method 800 may be performed at
least partially, for instance, using the matcher 4404 of FIG. 44.
Initially, as shown at block 802, a search query is received. One
or more terms are identified from the search query, as shown at
block 804. It should be understood that throughout this
description, when terms are identified from a search query, the
terms may include the exact terms contained in the received search
query. Alternatively or additionally, the terms may include other
terms identified from query augmentation. The other terms may
include, for instance, correct spellings for misspelled terms,
alternative forms of terms, and synonyms.
[0097] Bit vectors corresponding to the one or more terms from the
search query are identified, as shown at block 806. Each term may
be included in multiple bit vectors, and each bit vector or a
portion of the bit vectors containing each term may be identified.
The bit vectors are intersected to identify matching documents, as
shown at block 808. The bit vectors associated with a single term
are intersected to identify documents matching that term. Bit
vectors associated with distinct terms are combined using a
combination of intersection, union, and negation, as specified by
the query.
[0098] In some instances, matching documents may be identified by
intersecting all identified bit vectors. In other instances,
matching documents may be identified by intersecting different
subsets of identified bit vectors. This depends on how the query is
formulated by the matcher. For instance, if a query only contains
"and" operators, all bit vectors are intersected. As a specific
example, suppose a query is performed to identify documents that
contain "large" and "collection." In this case, bit vectors
containing the term "large" would be intersected with bit vectors
containing the term "collection." Documents corresponding to bits
set in each of those bit vectors are determined to be matching
documents. If a query containing an "or" operator is performed,
matching documents may be identified by intersecting different
subsets of bit vectors. For example, suppose a query is performed
to identify documents that contain "large" or "larger" in
conjunction with "collection." Matching documents may be identified
from both the intersection of bit vectors containing the term
"large" with bit vectors containing the term "collection" and from
the intersection of bit vectors containing the term "larger" with
bit vectors containing the term "collection."
[0099] In some configurations, the bit vectors identified for a
search query may be intersected in any order at block 808 of FIG.
8. However, in other configurations, the order in which the bit
vectors are intersected may be configured to provide more efficient
processing. As discussed previously, the bit vectors for each term
may include both short row bit vectors and long row bit vectors.
Additionally, as discussed above, for initial intersections, each
bit in the intersected bit vectors is processed. However, after a
certain number of intersections, there is no need to scan the
entire bit vectors when performing additional intersections.
Instead, those additional intersections may be performed, for
instance, by probing random locations in the bit vectors.
Accordingly, some configurations may improve the efficiency of the
intersection process by initially intersecting short rows. FIG. 9
provides a flow diagram showing a method 900 for intersecting bit
vectors using short bit vectors first. The method 900 may be
performed at least partially, for instance, using the matcher 4404
of FIG. 44. As shown at block 902, short row bit vectors are
identified from the set of bit vectors to be intersected. At least
a portion of the short row bit vectors are intersected before
intersecting any long row bit vectors, as shown at block 904. In
some configurations, all short row bit vectors are intersected
before intersecting long row bit vectors. In other configurations,
some long row bit vectors may be processed before some short row
bit vectors. When intersections are subsequently performed in which
each bit needs to be processed, the short row and long row bit
vectors may be intersected in any order. Any and all such
variations are contemplated to be within the scope of aspects of
the technology described herein.
[0100] By way of example to illustrate the processing of
intersecting short row bit vectors first, suppose a query is
performed for the terms "large" and "hadron" and "collider." As
shown in FIG. 10, the term "large" has an IDF of 1.16 and is
included in two short row bit vectors 1002, 1004 and one long row
bit vector 1006. The term "hadron" has an IDF of 3.71 and is
included in four short row bit vectors 1008, 1010, 1012, 1014 and
one long row bit vector 1016. The term "collider" has an IDF of
3.57 and is included in four short row bit vectors 1018, 1020,
1022, 1024 and one long row bit vector 1026.
[0101] As shown in FIG. 11, the matcher may logically arrange the
bit vectors for intersection with the short bit vectors first
followed by the long row bit vectors. In the example of FIG. 11,
the short row bit vectors from the three terms have been
alternated. However, it should be understood that the short row bit
vectors may be ordered in any manner. For instance, the short row
bit vectors for the term "large" may be arranged first followed by
the short row bit vectors for the term "hadron" followed by the
short row bit vectors for the term "collider."
[0102] In the example shown in FIG. 11, the matcher intersects each
of the short row bit vectors and then intersects the result with
the long row bit vectors. When intersecting bit vectors of the same
length, a bit from the first bit vector is intersected with the
corresponding bit in the second bit vector (e.g. the first bits are
intersected, the second bits are intersected, the third bits are
intersected, etc.). For instance, these intersections may be
performed 64 bits at a time by the CPU. However, when intersecting
bit vectors of different lengths, a bit from the shorter bit vector
corresponds to multiple bits in in the longer bit vector. For
instance, when intersecting a short row bit vector having four
documents per bit with a long row bit vector having one document
per bit, a bit from the short row bit vector is separately
intersected with each of four corresponding bits in the long row
bit vector.
[0103] As noted previously, some queries may be processed that
require different subsets of bit vectors to be intersected. For
example, suppose the search query "large hadron collider" is
augmented to form the query "(large or larger) and hadron and
(collider or collide)." This query would involve the following
combinations of bit vector intersections: (1) large and hadron and
collider; (2) large and hadron and collide; (3) larger and hadron
and collider; and (4) larger and hadron and collide. When
performing such queries, the bit vector intersections may be
ordered such that intersections common to multiple bit vector
intersection combinations are performed first and the results of
these common intersections saved so they may be reused.
[0104] FIG. 12 illustrates the concept of ordering bit vector
intersections such that intersections common to multiple bit vector
combinations are performed first and the results reused. As shown
in FIG. 12, the search query "large hadron collider" 1202 is
processed to form the query "(large or larger) and hadron and
(collider or collide)" 1204. In the present example, each term
includes two short rows and one long row. As such, the augmented
query 1204 could be represented with the bit vectors for each term
as shown in the expression 1206 in which: "A" and "a" corresponds
to the term "large;" "B" and "b" corresponds to the term "larger;"
"C" and "c" corresponds to the term "hadron;" "D" and "d"
corresponds to the term "collider;" and "E" "e" corresponds to the
term "collide." Short row bit vectors are denoted by lower case,
and long row bit vectors are denoted by upper case. For instance,
a.sub.1 and a.sub.2 correspond with two short row bit vectors for
the term "large," and A.sub.1 corresponds to a long row bit vector
for the term "large."
[0105] The expression 1206 can be written to form the expression
1208 by pulling short row bit vectors and those bit vectors for
terms common to combinations to the left. For instance, the term
"hadron" (represented by "c" in the expression 1208) is included in
all combinations, while the terms "large" and "larger" (represented
by "a" and "b" in the expression 1208) are each included in two
combinations. Note that the terms "collider" and "collide"
(represented by "d" and "e" in the expression 1208) are also each
included in two combinations such that, in an alternative
formulation, the locations in the expression 1208 for "a" and "b"
could be exchanged with "c" and "d" and vice versa.
[0106] The expression 1208 could be represented using a tree 1210,
which could also be shown as the tree 1300 in FIG. 13 in which each
block corresponds to a bit vector. Representing the expression 1208
as in the tree 1300 illustrates the order in which the
intersections are performed. As shown in FIG. 14, the two short row
bit vectors for the term "hadron" (c.sub.1 and c.sub.2) are
initially intersected and the results may be saved. This allows the
results from that intersection to be reused as will be discussed
below. The results are further intersected with the two short row
bit vectors for the term "large" (a.sub.1 and a.sub.2) and the
results may be saved. This allows the results from the
intersections of these four bit vectors to be reused as will be
discussed below. Those results are further intersected with the
short row bit vectors for the term "collider" (d.sub.1 and d.sub.2)
and the long row bit vectors for each of those three terms
(A.sub.3, C.sub.3, and D.sub.3). A set of documents matching the
terms "large," "hadron," and "collider" are found from these
intersections.
[0107] As shown in FIG. 15, the results from the intersections of
the short row bit vectors for the terms "hadron" and "large"
(c.sub.1, c.sub.2, a.sub.1, and a.sub.2) generated as discussed
above with reference to FIG. 14 may be reused and intersected with
the short row bit vectors for the term "collide" (e.sub.1 and
e.sub.2) and the long row bit vectors for the three terms (A.sub.3,
C.sub.3, and E.sub.3). A set of documents matching the terms
"large," "hadron," and "collider" are found from these
intersections.
[0108] As shown in FIG. 16, the results from the intersections of
the short row bit vectors for the term "hadron" (c.sub.1 and
c.sub.2) generated as discussed above with reference to FIG. 14 may
be reused and intersected with the short row bit vectors for the
term "larger" (b.sub.1 and b.sub.2) and the results may be saved so
they may be reused as will be discussed below. Those results may be
further intersected with the short row bit vectors for the term
"collider" (d.sub.1 and d.sub.2) and the long row bit vectors for
the three terms (B.sub.3, C.sub.3, and D.sub.3). A set of documents
matching the terms "larger," "hadron," and "collider" are found
from these intersections.
[0109] As shown in FIG. 17, the results from the intersections of
the short row bit vectors for the terms "hadron" and "larger"
(c.sub.1, c.sub.2, b.sub.1, and b.sub.2) generated as discussed
above with reference to FIG. 16 may be reused and intersected with
the short row bit vectors for the term "collide" (e.sub.1 and
e.sub.2) and the long row bit vectors for the three terms (B.sub.3,
C.sub.3, and E.sub.3). A set of document matching the terms
"larger," "hadron," and "collide" are found from these
intersections.
[0110] FIG. 18 provides a flow diagram that illustrates a method
1800 for a matcher, such as the matcher 4404 of FIG. 44, to
generate a matcher plan that provides an efficient order for
intersecting bit vectors. Initially, as shown at block 1802, a
search query is received. Terms are identified from the search
query, as shown at block 1804.
[0111] Available bit vectors corresponding to the one or more terms
from the search query are identified, as shown at block 1806. Each
term may be included in multiple bit vectors, and each bit vector
or a subset of the bit vectors containing each term may be
identified. This may involve, for instance, identifying how many
short bit vectors are available for each term, the length of each
short bit vector, and how many long bit vectors are available for
each term.
[0112] A matcher plan is generated at block 1808 to provide an
order for intersecting bit vectors for each of the terms. The order
of the matcher plan may be generated to provide for efficient
matching, as described above. For instance, short bit vectors may
be intersected before long bit vectors. As another example, the bit
vector intersections may be ordered with intersections common to
multiple bit vector intersection combinations being performed first
and the results of the intersections saved so they may be reused.
The bit vectors are intersected at block 1810 to identify matching
documents.
Compiling Query Plans to Machine Code
[0113] A matcher plan (or query plan) generated for a given search
query may provide a tree of nodes with various operations, such as
"and," "or," and "not" operations, and identify bit vectors to
intersect. Running such a matcher plan may involve applying all
those operations to identified bit vectors. One approach to
processing the matcher plan may be to interpret the tree, meaning
that the tree would be traversed and each node evaluated. However,
there can be significant overhead in traversing that tree and
evaluating each node. Consider a hypothetical example where the
tree is interpreted. In such an example, there would be some cost
associated with determining the type of each node as it is visited,
which is a step necessary in understanding how to evaluate the
node. There would also be some cost associated with storing and
enumerating the set of children associated with each node. Each of
those actions takes time to process and creates overhead.
[0114] The cost of the actual work done at nodes to intersect bit
vectors is relatively small. For instances, the work may consist of
only two instructions--ANDing a value into an accumulator and
branching to terminate evaluation if the accumulator is zero. The
cost of determining the node type and how many children nodes there
are for the node is actually higher than the cost of the bit vector
intersection. Therefore, this presents a circumstance in which the
overhead of interpretation is greater than the actual work (i.e.,
intersecting bit vectors). Furthermore, to process the matcher
plan, the tree may be evaluated many times (e.g., thousands or even
millions of times) such that each node may be repeatedly analyzed
during the processing, creating additional overheard.
[0115] To address this issue, the matcher plan may be compiled into
machine language. In particular, a JIT (just in time) compiler may
be used that processes a matcher plan to generate machine code. For
example, in search systems that employ x64-based processors (e.g.,
XEON processors), the JIT compiler may compile the matcher plan to
x64 code. The JIT compiler is done at the time a search query is
received from a user. The process of performing a query in such
configurations may comprise receiving a search query, generating a
matcher plan based on the search query, and converting the matcher
plan into machine code.
[0116] When a matcher plan is JIT compiled, the process may include
walking over the matcher plan similar to the way an interpreter
would. The fundamental difference, though, is the JIT compiler only
examines each node once because it outputs the code the process
should do as it walks over the tree. That code can then be
repeatedly run (e.g., thousands or even millions of times) and
running the code doesn't have the overhead of the evaluation method
used by the interpreter.
[0117] Processors have a variety of resources available to them
(e.g., bus between processor and memory, fixed size cache inside
processor that holds data brought in from memory, certain number of
processor cores with a certain number of transistors that can do a
certain amount of work, etc.). Typically, for any give program,
speed is limited by one of the resources. The reason it's limited
by one of the resources is that as soon as it's limited by that
resource, it's not going fast enough to be limited by the next most
precious resource. Different programs are limited by different
things. A fundamental limitation of big index search is accessing a
lot of data. In general with processors that exist today, the
fundamental limit is how quickly data can be moved through the
processor by the memory bus. With posting lists, the complexity of
the algorithm leads to a situation in which the CPU becomes
saturated before the memory bus. A value of some aspects of the
technology described herein is that the code is simple enough that
data can be processed by the CPU faster than the memory bus can
supply the data, and as a result, the memory bus can be
saturated.
[0118] Thus, in some aspects of the technology described herein,
one design goal may be to saturate the memory bus, which means
there's a certain amount of information to bring into the algorithm
and the goal is to have the system limited by the amount of
information and the ability of the memory bus to bring in
information to the processor. This design goal would avoid being
limited by the overhead of processor instructions. In other words,
the goal is to have the processor waiting on memory and not the
other way around. Even though processors are getting more and more
cores, it's still hard to keep the memory bus saturated, as the
memory busses are getting faster and wider. As a result, the number
of instructions between each memory bus access may be limited to as
few as two or three instructions in order to saturate the memory
bus. JIT compiling the matcher plan provides machine code that
limits the number of instructions to help achieve this goal.
[0119] It should be noted that the use of JIT compilation to help
achieve the design goal of saturating the memory bus may be useful
in systems employing certain existing processors, such as XEON x64
processors. However, other hardware designs may be employed, such
as field-programmable gate arrays. Because the cost structures of
other hardware designs may be different, JIT compilation may not be
as useful for those designs.
Query IDF Boosting
[0120] As discussed previously, search systems typically employ a
matcher that identifies documents containing query terms (i.e.,
"matching documents") followed by a ranker that ranks at least some
of the matching documents. One of the variables that impacts the
run time of searches performed by such search systems is the number
of matching documents returned by the matcher. If a large number of
matching documents is returned, it may take an unacceptable amount
of time to rank each of those documents.
[0121] Accordingly, the performance of a search system for a given
query may be viewed as a function of the number of matching
documents that may be returned by the matcher for the query. One
way to view this is by reference to the IDF of the query. The IDF
of a query may be determined by taking the logarithm of the total
number of indexed documents divided by the number of matching
documents for the query. For instance, a query that has an IDF of
four would return one matching document out of every 10,000
documents in the corpus. For a given search query, the IDF of the
query represents the number of possible matching documents from a
corpus of documents.
[0122] A search system employing a search index with bit vectors in
accordance with aspects of the technology described herein may
perform well for search queries that result in an acceptable number
or percentage of matching documents. What is considered an
acceptable number or percentage of matching documents may be
configurable based on the design optimizations of the search
system. In some configurations, this corresponds to queries with an
IDF of about 4 or greater (i.e., 1 in 10,000 or fewer documents
match the queries). For such search queries, ranking may be
inexpensive enough that the matcher may process the search queries
and return matching documents without any modifications to the
matching process.
[0123] For search queries that would return an unacceptable number
or percentage of matching documents (e.g., queries with an IDF of
less than 4), some configurations employ techniques to reduce the
number of matching documents. These techniques are referred to
herein as "query IDF boosting" as a reduction in matching documents
for a search query results in a higher IDF for the query. A general
technique that may be employed by some configurations to reduce the
number of matching documents for a search query is to intersect one
or more additional bit vectors during the matching process for the
search query. These additional bit vectors are referred to herein
as "strengthening row" bit vectors since the additional bit vectors
strengthen the matching process by reducing the number of matching
documents (i.e., boosting the IDF of the query).
[0124] In some aspects of the technology described herein, a
strengthening row bit vector may be based on static rank of
documents. As is known in the art, static rank refers to document
ranking features that are independent of the search query. For
instance, one static rank feature often used by search engines is
ranking a document based on the number of other documents that
contain hyperlinks to the document. The more links to the document
may be viewed as being indicative of higher importance and
therefore a higher rank. Because static ranks of documents are
query independent, they can be determined at index generation time
when information about a document is being added.
[0125] To support query IDF boosting, a bit vector may be added to
the search index to identify documents that have the highest static
rank in the document corpus. This static rank bit vector may be
generated by determining the static rank of documents in the
corpus. Based on the static rank, a certain number or percentage of
documents with the highest static rank may be identified. For
instance, the top 10% static rank documents may be identified or
documents with static rank score above a selected static rank score
threshold. A bit vector is generated in which bits are set for each
of the highest static rank documents (e.g., the top 10% static rank
documents), while bits are left cleared for the other documents
(e.g., the remaining 90% of the documents). As such, when a search
query is performed, if the matcher determines that an unacceptable
number of matching documents will be returned, the matcher may also
intersect the static rank bit vector. Since bits are set for only
the highest static rank documents in the static rank bit vector,
intersecting that bit vector will result in only documents from the
highest static rank documents that match the terms in the search
query to be returned as matching documents. In essence, using the
static rank bit vector limits the pool of possible documents to the
highest static rank documents.
[0126] Another query IDF boosting approach is to use strengthening
rows for non-body information. Generally, terms for a document may
be identified in variety of different locations. Terms may be
identified from the body of the document, but terms may also be
identified from other non-body locations. For instance, non-body
locations for a document may contain anchor text (i.e., the text of
a hyperlink within another document that links to the document),
the URL of the document, the title of the document (i.e., the words
that are presented in a title bar of a browser), and search
information such as the terms of search queries that resulted in
the document being selected from search results by a user and terms
included in a snippet (i.e., summary/synopsis) of the document.
[0127] Non-body information is often viewed as providing a better
indicator of relevance than body information. Accordingly, limiting
a matcher to only non-body information reduces the result set while
yielding documents are likely more relevant. In accordance with
some aspects of the technology described herein, non-body
information may be indexed in bit vectors. This may be done by
identifying terms that appear in non-body locations and indexing
those terms with information identifying the terms as non-body
terms (i.e., terms appearing in a non-body location). As a result,
the bit vectors index information identifies not only terms
generally (i.e., terms from body and non-body locations) but also
non-body terms (i.e., terms only in non-body locations). The
general terms and non-body terms may be distributed throughout the
search index. For instance, a particular bit vector may include
both general terms and non-body terms.
[0128] In accordance with some aspects of the technology described
herein, when a search query is performed, the matcher initially
intersects bit vectors for general terms (i.e., terms from body and
non-body locations) corresponding to terms from the query to
estimate the number of matching documents that will be returned. If
the matcher estimates that an unacceptable number of matching
documents will be returned, the matcher identifies and intersects
bit vectors for non-body terms corresponding to terms from the
query. In essence, this limits the matching documents to documents
that contain the query terms in non-body locations.
[0129] Referring to FIG. 19, a flow diagram is provided that
illustrates a method 1900 for matching documents using
strengthening rows. The method 1900 may be performed at least
partially, for instance, using the matcher 4404 of FIG. 44. As
shown at block 1902, a search query is received. One or more terms
are determined based on the search query, as shown at block 1904.
The one or more terms may comprise terms explicitly set forth in
the search query and/or terms determined based on terms in the
search query (e.g., misspellings, alternative forms, synonyms,
etc.).
[0130] A determination is made at block 1906 regarding whether the
matcher is likely to return an unacceptable number of matching
documents for the search query. The determination may be based on
any combination of methods in accordance with different aspects of
the technology described herein. In some configurations, the
determination is based on the IDF of terms from the search query.
In some configurations, the determination is based on sampling. As
an example to illustrate, the determination may be made by
beginning the matching process by identifying bit vectors for the
terms identified at block 1904 and intersecting those bit vectors.
The bit vectors identified and intersected at this point may
include bit vectors with general terms (i.e., terms from body and
non-body locations) corresponding to the terms identified at block
1904. During the matching process, the number of matching documents
likely to be returned may be estimated based on the percentage of
documents being returned as matching. This could involve running
the full plan over a fraction of the index and then using the
observed match rate to predict a total number of matches that would
be returned if ran over the entire index.
[0131] If it is determined at block 1908 that an acceptable number
of matching documents is likely to be returned, the matching
process may be performed without using any strengthening row bit
vectors to reduce the number of matching documents, as shown at
block 1910. Alternatively, if it is determined at block 1908 that
an unacceptable number of matching documents is likely to be
returned, one or more strengthening row bit vectors may be selected
at block 1912 and intersected to reduce the number of matching
documents, as shown at block 1914.
[0132] Any number and type of strengthening row bit vectors may be
selected and intersected. For instance, a static rank bit vector
may be selected and intersected to restrict possible matching
documents to the top static rank documents. As another example, bit
vectors having non-body terms corresponding to the terms identified
at block 1904 may be intersected to restrict possible matching
documents to documents that contain the terms in non-body
locations. This may be done for all terms identified at block 1904
or only a subset of the terms. It should be understood that other
types of strengthening row bit vectors may be selected and
intersected during the matching process to reduce the number of
matching documents.
[0133] In some aspects of the technology described herein, the
number and/or type of strengthening row bit vectors to intersect
may be selected based on the estimated number of matching
documents. Also, different terms may have more or less
strengthening rows compared to other terms from the search query.
Different strengthening row approaches may provide different
reductions in matching documents. For queries that will likely
result in a higher number of matching documents, strengthening rows
that provide a greater reduction of matching documents may be
selected. For instance, based on the estimated number of matching
documents, a static rank bit vector, one or more bit vectors with
non-body terms, or both a static rank bit vector and one or more
bit vectors with non-body terms may be selected and
intersected.
[0134] While the method 1900 of FIG. 19 shows only a single
determination regarding whether the number of matching documents is
likely to be unacceptable, the determination may be made repeatedly
as the matching process continues. For instance, an initial
determination may indicate that an acceptable number is likely to
be returned. However, upon further matching, it may be determined
that an unacceptable is now likely, and strengthening rows may be
selected based on that redetermination.
[0135] Some search queries may have such low IDFs (i.e., return a
particularly large number of matching documents) that strengthening
row approaches may not sufficiently limit the number of matching
documents. For such search queries, the search engine may cache
search results for those search queries. Therefore, when a search
query is received that is cached, the cached search results are
simply retrieved.
[0136] Accordingly a variety of different techniques may be
employed during the matching process to control the number of
matching documents returned. The techniques may be selected based
on an estimated number of matching documents determined for the
given search query. By way of example only and not limitation, one
specific configuration employs different techniques for search
queries based on different ranges of IDF. In this example
configuration, search queries with an IDF less than 2, cached
results are used. For search queries with an IDF between 2 and 3, a
static row bit vector and one or more bit vectors with non-body
terms are intersected. For search queries with an IDF between 3 and
4, bit vectors with non-body terms are intersected. Finally, for
search queries with an IDF over 4, the matching process is
performed without any strengthening row bit vectors being added to
reduce the number of matching documents.
Phrases in Search Index
[0137] Some search queries include specific phrases. Phrases are an
important concept for search engines because documents that have a
collection of terms in different locations of the documents may not
be as relevant as documents that contain the phrase. For instance,
consider the phrases: "The The" (a band) and "to be or not to be."
While the terms included in these phrases are common, the phrases
themselves are considerably rarer.
[0138] Generally, if information is not indexed that allows for the
identification of phrases in documents, the matcher may identify
documents that contain the terms of the phrase but not the phrase.
If the ranker also doesn't consider phrases, the documents without
the phrase may by ranked higher than other documents that contain
the phrase although the documents that contain the phrase may be
considered better results from the user's perspective.
Additionally, if not limited to a maximum number, the number of
documents sent by the matcher to the ranker may be large, resulting
in an unacceptable amount of time to rank all the documents.
Alternatively, if a limit is placed on the number of documents sent
to the ranker, the matcher may select documents that contain the
terms in different locations while excluding documents that contain
the phrase.
[0139] A posting list system has the option to use positional
posting lists that store information regarding not only the
presence of a term in a document but the position of the term in
the document. Therefore, phrases may be identified by using the
position information to determine words are adjacent and therefore
form a phrase. However, a large amount of storage is required to
store the positional information, and it is CPU intensive to
collate the positions of terms to discover phrases.
[0140] The bit vector approach employed by aspects of the
technology described herein does not store positional information
in the index and therefore cannot identify phrases using the same
approach as in a positional posting list system. As a result,
aspects of the technology described herein may instead store
phrases in bit vectors to allow for the identification of documents
that contain phrases set forth in search queries. As used herein, a
phrase refers to any combination of two or more words, such as an
n-gram, an n-tuple, a k-near n-tuple, etc. An n-gram is a sequence
of "n" number of consecutive or almost consecutive terms. An n-gram
is said to be "tight" if it corresponds to a run of consecutive
terms and is "loose" if it contains terms in the order they appear
in the document, but the terms are not necessarily consecutive.
Loose n-grams are typically used to represent a class of equivalent
phrases that differ by insignificant words (e.g., "if it rains I'll
get wet" and "if it rains then I'll get wet"). An n-tuple, as used
herein, is a set of "n" terms that co-occur (order independent) in
a document. Further, a k-near n-tuple, as used herein, refers to a
set of "n" terms that co-occur within a window of "k" terms in a
document.
[0141] Phrases may be stored in bit vectors similar to the
discussion above for terms. As a result, each bit vector in an
index may store any combination of terms and phrases. A difference
between phrases and terms, though, is that phrases don't need to be
stored in as many bit vectors as for terms. Instead, a phrase may
be stored, for instance, in a single short row bit vector. Because
a phrase contains information that overlaps significantly with the
terms in the phrase, intersecting the bit vectors for the terms and
a bit vector for the phrase may allow for identification of
documents containing the phrase. This is based on the concept of
strengthening row bit vectors discussed above with reference to
query IDF boosting. For phrases, a weaker query would be simply
intersecting bit vectors for the individual terms. However, the
query may be strengthened by also intersecting one or more bit
vectors for the phrase. This makes phrases inexpensive to store in
a search index using bit vectors and provides an advantage over
other approaches, such as positional posting lists, which require a
significant amount of storage to account for phrases.
[0142] FIG. 20A provides an example to illustrate the concept of
using bit vectors containing a phrase as strengthening row bit
vectors. The present example illustrates a query for the phrase
"easy street." The terms "easy" and "street" are both very common
with an IDF of 1.27 and 1.14, respectively. Because the terms are
common, they don't need many bit vectors to encode information for
the terms. In the present example, a rule of thumb in which the
number of bit vectors for a term is the IDF rounded up has been
used such that the terms "easy" and "street" are each included in
two bit vectors. Additionally, each term is included in one short
row bit vector and one long row bit vector.
[0143] The phrase "easy street" is less common with an IDF of 4.07.
If the same rule of thumb were used, the phrase would be included
in five bit vectors, consisting of four short row bit vectors and
one long row bit vector. If that many bit vectors were used for
phases, a considerable amount of storage would be required for
phrases. However, the bit vectors for "easy street" have a lot of
commonality with the bit vectors for "easy" and the bit vectors for
"street."
[0144] As shown in FIG. 20B, if a query is performed for "easy
street," the bit vectors for "easy" and "street" are used since, at
a minimum, documents matching the query must contain those terms.
As can be seen in FIG. 20B, the bit vectors from those terms
provide intersections of four bit vectors, which is sufficient to
remove noise. As a result, five bit vectors for "easy street" are
not needed to identify matching documents. Instead, only two bit
vectors are used to identify matching documents containing the
phrase "easy street." Therefore, the search index doesn't need
store the three bit vectors 2006, 2008, 2010 for the phrase "easy
street." Instead, the search index only stores the two short row
bit vectors 2002, 2004.
Shards in Search Index
[0145] Documents indexed by search engines typically vary greatly
in length, where length of a document is measured by the number of
unique words in the document. On one end of the spectrum, a
document may contain only a single word; while on the other end of
the spectrum, a document (e.g., a dictionary) could conceivably
have almost every word. In the context of using bit vectors to
index documents, short documents have a small percentage of bits
set across the bit vectors, while long documents have a large
percentage of bits set. One issue that is created for bit vectors
is that efficiencies are lost when dealing with documents of
sufficiently varying length. The desired bit density is achieved
for one length only. Too much variance in document length drives
bit density to be too high in some places and too low in
others.
[0146] By way of illustration, FIG. 21 illustrates a portion of a
search index showing only long row bit vectors (i.e., one document
per bit). The highlighted column 2102 corresponds to a long
document. As can be seen in FIG. 21, almost all bits are set based
on the presence of the underlined terms, which appear in the
document. Although most of the bits are set for the long document,
there are many terms (i.e., the non-underlined terms in FIG. 21)
that are not present in the document. As a result, search queries
that include terms not in the document (i.e., the non-underlined
terms in FIG. 21) but sharing a bit vector with a term in the
document will match to the long document even though the long
document is not a true match for the term. As can be understood,
the likelihood of false positives goes up with documents that
create greater than target bit density.
[0147] Some configurations address this issue of varying document
lengths by breaking/partitioning the index into different sections
or "shards" of the search index. Each shard indexes documents with
lengths corresponding to a different range of document length. For
instance, documents with 0-100 terms may be assigned to a first
shard, documents with 101-200 terms could be assigned to a second
shard, documents with 201-300 terms could be assigned to a third
shard, etc.
[0148] By providing different shards, documents within each shard
are within a range of document length that prevents inefficiencies
created by a wide discrepancy in document length. Each shard may
have different term assignments to bit vectors to control bit
densities in each column (i.e., the percentage of bits sets in each
column). On shards with longer documents, fewer terms may be shared
in bit vectors to control the column bit density. In other words,
the longer shards may have fewer terms per bit vector. Conversely,
on shards with shorter documents, more terms may be shared in bit
vectors (i.e., higher terms per bit vector).
[0149] FIG. 22 illustrates a method 2200 for generating shards for
a search index using bit vectors. The method 2200 may be performed
at least partially, for instance, using the indexer 4418 of FIG.
44. As shown in block 2202, the number of shards to use is
determined. Additionally, as shown at block 2204, the range of
document lengths is determined for each shard. Although the
determination of the number of shards and the range of document
lengths for each shard are shown as separate blocks in FIG. 22, it
should be understood that those design parameters may be determined
in a single process. Based on the document length ranges, documents
are assigned to each shard according to their lengths, as shown at
block 2206. The search index is generated by storing bit vectors
for each shard on computer storage media, as shown at block 2208.
Each shard stores bit vectors that index terms for documents having
document lengths within the document length range for each
shard.
[0150] In some configurations, the determination of the number of
shards to employ and the document length range of each shard may be
based on two considerations. The first consideration is that there
is a fixed overhead per shard as each query needs to be performed
on each shard. As such, it is undesirable to have too many
shards.
[0151] The second consideration is that there is cost associated
with the amount of storage wasted by having documents of varying
length. In particular, given a desired column bit density (e.g.,
10%), if the longest document in a shard yields the desired column
bit density (i.e., 10%), the shorter documents will have a lower
column bit density. Any document with a column bit density below
the desired column bit density represents wasted storage. The
greater the variation in document length in a shard, the greater
the amount of wasted storage.
[0152] A cost function may be generated as a function of the two
above considerations. In particular, the cost function is
calculated as the number of shards multiplied by some weight factor
plus the cost of wasted storage created based on varying document
lengths in each shard. The weighting applied to the number of
shards may be configurable based on the relative importance of the
cost of processing required for additional shards (i.e., a speed
cost) versus the cost of wasted storage from having larger
variations in document lengths in the shards (i.e., a storage
cost). The cost of wasted storage may be computed, for instance, as
an approximation based on the total memory consumed (Longest
LengthNumber of Documents) or more particularly using the following
equation:
Longest LengthNumber of Documents-.SIGMA.Document Lengths Equation
3:
[0153] Solving the cost function may be viewed as an optimization
problem. As such, a variety of different algorithms may be employed
to solve the cost function to optimize the number of shards and the
range of document lengths for each shard. In some configurations,
the cost function is solved as an all pairs shortest path
problem.
[0154] When a search is received, a query may be performed on each
of the shards. The query on each shard returns a set of documents,
which are combined to provide a set of matching documents.
[0155] In some configurations, some of the work of preparing the
query for each shard may be shared. Generally, bit vectors for the
same terms are intersected for each of the shards. The main
difference among the shards is the mapping of terms to bit vectors.
For instance, a term in one shard may be included in three bit
vectors. For another shard, the term may be included in seven bit
vectors that are completely different from the bit vectors in the
first shard. Because the main difference for querying the different
shard is the mapping of terms to bit vectors, the structure of the
query and the query processing prior to converting terms to actual
bit vectors may be reused across the shards.
[0156] FIG. 23 illustrates a method 2300 for performing a search
using multiple shards. The method 2300 may be performed at least
partially, for instance, using the matcher 4404 of FIG. 44. As
shown at block 2302, a search query is received. One or more terms
are identified for the search query, as shown at block 2304. The
one or more terms may comprise terms explicitly set forth in the
search query and/or terms determined based on terms in the search
query (e.g., misspellings, alternative forms, synonyms, etc.).
[0157] A generic query plan is generated based on the identified
terms, as shown at block 2306. The generic query plan may generally
set forth a process for intersecting bit vectors containing terms
from the search query. The generic query plan is converted to a
shard specific query plan for each shard, as shown at block 2308.
Each shard specific query plan is then performed on each
corresponding shard to identify matching documents for the search
query, as shown at block 2310.
[0158] Converting the generic query plan into each shard specific
query plan is similar through most stages on each shard. Generally,
after the query is parsed, a set of terms is identified and the
terms are mapped to a set of bit vectors for each shard. The main
thing that's different between the shards is the mappings from
terms to bit vectors are different. For example, a term in one
shard may be in three bit vectors (e.g., row 7, 10, and 15). In
another shard, the term may appear in 10 bit vectors, which may be
totally different rows from the first shard. Everything before
converting terms to rows may be reused across all the shards. Even
if the mappings from terms to rows are different across the shards,
the structure of the query may remain the same. In other words, for
every term, there is a set of short row(s) and long row(s) and the
way the short rows are pulled to the left and the long rows to the
right is the same across the shards although the number of rows and
the identifier of rows are different.
[0159] In some configurations, the generic query plan may include a
maximum number of short rows for each term and a maximum number of
long rows for each term. The planner may initially be run without a
specific mapping between terms and rows (rows are essentially
virtual rows with no mapping to physical rows in index). The plan
for a specific shard may be generated by replacing each of these
virtual rows with a physical row specific for that shard. On each
different shard, a different set of physical rows would be used.
When the generic query plan has a maximum number of short and long
rows, not all shards may use all of these virtual rows (i.e., plan
rows). To address this, as virtual rows are replaced with physical
rows, unused virtual rows are replaced with physical rows that do
not impact semantics of the Boolean expression of the query may be
used as filler rows. For example, physical rows that have all ones
or duplicates of one or more of the physical rows already included
could be used for those extra rows.
[0160] So a generic query plan can very quickly be customized for
each of the shards. In some configurations, the matching engine
gets two inputs: code that's been compiled that runs and a table
that has pointers to all the rows it should use for a given shard.
Therefore, "customizing" a generic plan for each shard may simply
involve providing a different table of rows for each shard (i.e.,
the same code is used for each shard). In this way, the entire
query pipeline all the way down to the matcher may be generic with
respect to shards with the difference between shards being
expressed as a table with pointers to rows. As a result, much of
the planning work is reused.
[0161] One cost of the above approach of reusing the query plan for
the shards is that the generic query plan may reference more rows
than actually needed for some shards since the generic query plan
is intended to accommodate the maximum number of rows a shard may
need, which means for some shards, there is some wasted effort to
intersect placeholder or filler rows that are all ones or are
duplicates of previously used rows. However, this may be acceptable
for a number of reasons. The cost of the matcher is mainly scanning
the first few (e.g., four) rows, such that additional row
scanning/intersections don't add significant cost. Additionally, if
the approach simply reuses the row used last, cost of intersecting
that row is low because the row is already in the processor
cache.
Band Table
[0162] The frequency with which each term appears in a document
corpus may vary widely. Some terms are extremely common, while
other terms are extremely rare, even to the point of appearing once
in the document corpus. As discussed previously, the number of bit
vector intersections required for a given term to reduce noise to
an acceptable level varies with term frequency. Generally, common
terms (i.e., terms with a high frequency in the document corpus)
need fewer bit vector intersections, while rare terms (i.e., terms
with a low frequency in the document corpus) need more bit vector
intersections.
[0163] When building a bit vector search index, a number of
different approaches may be taken for determining the bit vector
configuration for terms in accordance with various aspects of the
technology described herein. As used herein, the "bit vector
configuration" for a term sets forth the number and length of bit
vectors used for the term. The bit vector configuration may also
identify the class(es) of storage (e.g., RAM, SSD, and/or HDD) on
which to store each bit vector in the configuration.
[0164] One approach that may be employed is a "one size fits all"
approach in which all terms have the same bit vector configuration.
However, this approach has some drawbacks. In particular, if the
"one size fits all" bit vector configuration specifies a higher
number of bit vectors to account for low frequency terms, storage
is wasted as the higher number of bit vectors is not needed for
higher frequency terms. For instance, suppose the rarest term in
the index needs 10 bit vectors to adequately reduce noise for the
term, and consequently 10 bit vectors are used for each term in the
index. If the number of bit vectors needed for more common terms is
much smaller, then using 10 bit vectors for each of those common
terms wastes storage.
[0165] Alternatively, if the "one size fits all" bit vector
configuration specifies a lower number of bit vectors, lower
frequency terms may not have a sufficient number of bit vectors to
adequately reduce noise. For instance, suppose the most common term
only needs two bit vectors. For less common terms, using only two
bit vectors would not adequately reduce noise and there would be an
unacceptable number of false positives for those terms.
[0166] Another approach is using a custom bit vector configuration
for each term. In other words, each term is treated individually
when assigning bit vector configurations to the terms. While this
is possible and may be employed in some aspects of the technology
described herein, particularly when indexing a small corpus of
documents with fewer distinct terms, it may not be practical for
very large document collections with a large number of distinct
terms. For instance, when dealing with a very large number of
terms, the data structure required to map each term to its custom
bit vector configuration would be massive.
[0167] Still another approach is to assign bit vector
configurations to groups of terms clustered into different bands
(i.e., equivalence classes) based on term characteristics and
assigning a particular bit vector configuration to each band. In
other words, a "band" is a group of terms that have similar enough
term characteristics to be assigned the same bit vector
configuration. For instance, the bit vector configuration for one
band may specify two rank-6 bit vectors, one rank-3 bit vector, and
one rank-0 bit vector. Each term that has characteristics matching
those of that band will use that bit vector configuration. As used
herein, a "band table" may be used to store mappings of term
characteristics to bit vector configurations for each band employed
by the search index. Any number of band tables may be employed by a
search index. Additionally, any data structure may be used to store
the mappings for the band table.
[0168] Any term characteristic that impacts the number and/or
length of bit vectors and/or the class of storage used to store the
bit vectors for each term may be used to define the bands. In some
aspects of the technology described herein, the term
characteristics may also be used at runtime when performing a
query, and therefore the term characteristics may be limited to
ones that may be quickly determined.
[0169] By way of example only and not limitation, bands may be
defined by the following term characteristics: classification, gram
size, IDF, IDF sum, and tier hint. Classification refers to the
term's location in a document (sometimes referred to as the
"stream"). These may include, for instance, the body, non-body, and
metawords (words not displayed but added to/indexed with a document
to provide metadata about the document such as the document's
language). Gram size refers to the number of words for the term
(e.g., one for a single-word term, two or more for a phrase). IDF
refers to the term's frequency. Because each shard indexes a
different collection of documents, the frequency of a particular
term may vary among the shards. In some configurations, it is only
feasible to determine the exact term frequency or a good
approximation of the term frequency is only determined for the most
common terms. For other terms, it is assumed that the term
frequency is below some threshold term frequency, and this
threshold term frequency is used a proxy for the term's frequency.
The IDF sum is used for phrases to approximate the frequency of the
phrases. It may be impractical to determine the actual frequency of
all phrases. Instead, some configurations may combine the
frequencies of the individual terms in the phrase to provide the
IDF sum for the phrase. This is an approximation which serves to
help partition phrases into groups. Tier hint is used to represent
how frequent a term appears in search queries. This may help
determine the class of storage to use. For instance, some terms are
common in documents but rarely used in search queries. Rows for
these terms may be stored on slower storage. It should also be
noted that in configurations using shards, each shard may have a
different term distribution so a given term may have a different
bit vector configuration in each of the shards.
[0170] The bit vector configuration assigned to each band is
configurable based on various design goals for the search index. In
some configurations, the design goals may include balancing index
storage consumption with relevance. Generally, increasing the
number of bit vectors in bands increases relevance by allowing for
more bit vector intersections to reduce noise but increases the
storage requirements for the search index. Conversely, reducing the
number of bit vectors for bands reduces storage but also reduces
relevance.
[0171] Given these tradeoffs between storage and relevance, one
approach to assigning the bit vector configurations to bands is to
attempt to minimize total storage consumption while ensuring that
relevance does not fall below some threshold. This may be viewed as
a cost/benefit optimization problem in which the cost is the amount
of storage consumed and the benefit is the relevance provided.
While the cost of storing extra bit vectors is reasonably linear,
the additional benefit provides rapidly diminishing returns after a
point.
[0172] For a given bit vector configuration assignment to bands,
the amount of storage consumed is relatively easy to compute for a
corpus. The number of terms in a band may be approximated based on
the frequencies of terms associated with the band. Additionally,
the number of postings the terms contribute may be determined based
on the frequency (e.g., IDF) of the terms. Given a particular bit
vector configuration assignment to the bands, the amount of storage
may be determined based on the number of postings in each band and
the bit vector configurations in each band.
[0173] The relevance metric may be determined in a number of
different ways in various aspects of the technology described
herein. Relevance may be determined as an aggregate value, such as
an average, observed in the context of a statistically significant
corpus and a statistically significant query log. However,
relevance may also consider minimum values, variances,
nth-percentile values, and even complete distributions.
[0174] In some aspects of the technology described herein, the
relevance metric may be based on the false positive rate expected
for a given bit vector configuration assignment to the bands. The
false positive rate reflects the number or percentage of matching
documents expected to be returned by the matcher that don't
actually match the query in the sense that the documents don't
contain one or more terms from the query. For instance, if the bit
vector intersections for a query yield 100 matching documents
(i.e., 100 1-bits result from the intersections) and 20 of those
matching documents are not actual matches, the false positive rate
is 0.2 or 20 percent. The matching documents that are true matches
are referred to herein as valid matching documents, while the
matching documents that are not true matches are referred to herein
as invalid matching documents.
[0175] While the false positive rate may be used as the relevance
metric in some configurations, the false positive rate has some
drawbacks. Generally, the false positive rate applies to the
matcher only and may be inadequate for predicting end-to-end
pipeline relevance. For example, the false positive rate doesn't
account for matcher designs that limit the number of matching
documents returned for each query. In instances in which the number
of matching documents available in a corpus for a query is below
the maximum number of documents returned by the matcher, the false
positive rate will be high despite an appropriate result of
matching documents. For instance, suppose a matcher is designed to
return no more than five matching documents per query. In instances
in which a query includes one or more rare terms, there may be only
one matching document in the entire corpus. However, because the
matcher is designed to return five matching documents, four of
those documents are necessarily invalid matches. As a result, the
false positive rate would be 80% although the matcher could not
have returned a better set of matching documents.
[0176] The false positive rate also doesn't account for valid
matching documents that are displaced by invalid matching
documents. For instance, suppose again that a matcher is designed
to return five matching documents for every query. In one instance,
suppose a corpus has five matching documents for a first query and
the matcher returns four valid matching documents and one invalid
matching document for the first query. In another instance, suppose
the corpus has four matching documents for a second query and the
matcher returns the four valid matching documents and one invalid
matching document for the second query. In both instances, the
false positive rate is the same (20%). However, for the first
query, one valid matching document was displaced by an invalid
matching document; while for the second query, no valid matching
documents were displaced. Although the false positive rates are the
same, the set of matching documents from the second query present
better relevance than the set of matching documents for the first
query.
[0177] Accordingly, some configurations employ an error rate based
on the fraction of valid matching documents that could have been
returned by the matcher but were displaced by invalid matching
documents. This error rate may serve as a better proxy for
predicting overall pipeline relevance than the false positive rate.
For instance, in the example above in which there is only one valid
matching document in the corpus and the matcher must return five
matching documents, the false positive rate was 80%. However, the
error rate based on displaced valid matching documents would be
zero, which more accurately reflects the relevance. In the example
above in which a first query has one valid matching document
displaced and the second query has no valid matching documents
displaced, the false positive rate was 20% for both queries.
However, the error rate based on displaced valid matching documents
would be 20% for the first query and zero for the second query,
which more accurately reflects the relevance.
[0178] In some aspects of the technology described herein, the
search system may be configured such that an additional
verification step is performed in which valid matching documents
are retained and invalid matching documents are removed. This would
remove the false positives returned by the matcher. For instance,
the matcher could be configured to allow for a greater number of
matching documents to be provided to the additional verification
step. This may be acceptable if the verification step does not
consume too much additional storage and/or require too much
additional processing time. In such configurations, the relevance
metric may be based on a "fix-up cost," which is the cost in
additional storage and/or processing time required to identify and
remove the invalid matching documents.
[0179] In order to optimize the bit vector configuration
assignments to bands, a cost function may be employed that is a
weighted sum of a relevance metric (e.g., false positive rate;
error rate; fix-up cost; or other metric) and storage requirements.
The weighting applied is configurable based on the relative
importance of relevance and storage to the design of the search
system. A variety of optimization algorithms may be employed that
use the cost function to optimize the bit vector configurations
assigned to each band. For instance, in some configurations, a
gradient descent algorithm may be employed to quickly converge on a
reasonable/locally optimal set of bit vector configuration
assignments to bands.
[0180] FIG. 24 provides a flow diagram showing a method 2400 for
generating a data structure, such as a band table, mapping term
characteristics to bit vector configurations. As shown at block
2402, term characteristics are assigned to each of a number of
bands. Additionally, as shown at block 2404, bit vector
configurations are assigned to each band. The term characteristics
and/or bit vector configurations may be assigned to each band as
discussed hereinabove. For instance, a cost function may be
employed to assign bit vector configurations to each band in manner
that is optimized for design goals of balancing storage consumption
with a relevance metric. A data structure is generated at block
2406 that maps term characteristics to a bit vector configuration
for each band. The data structure may be used to identify bit
vector configurations for terms for a number of purposes, such as,
for instance, generating a bit vector-based search index, indexing
information about documents in the search index, and accessing bit
vectors for terms during a matching process for a search query.
Term Table
[0181] In addition to assigning bit vector configurations to terms,
bit vector locations in storage are mapped to terms to allow for
both indexing documents and performing queries in accordance with
aspects of the technology described herein. When indexing a
document, terms are identified in the document and the bit vector
locations for those terms need to be identified to set the bits in
the column for the document. When performing a search query, terms
are identified from the query and the bit vector locations for
those terms need to be identified for retrieving the bit vectors to
perform bit vector intersections. Accordingly, in either case,
given a term, the storage location of the bit vectors for the term
need to be identified.
[0182] While the bit vector configuration for a term identifies the
number and length of bit vectors to use for the term (and possibly
the class of storage to use), a mapping can be used that identifies
the actual/specific storage locations for those bit vectors. For
instance, a bit vector configuration for a term may indicate to use
three rank-6 bit vectors, 1 rank-3 bit vector, and 1 rank-0 bit
vector. The mapping associates the term (or its hash) with the
storage locations for each for those five bit vectors. The mapping
of storage locations for terms is referred to herein as a "term
table." Any number of term tables may be employed by a search
index. Additionally, any data structure may be used to store the
mappings for the term table.
[0183] One approach for mapping storage locations for terms is to
provide explicit mappings that identify specific bit vector
locations in storage for each indexed term. For a given term, an
explicit mapping associates a term (or its hash) with bit vector
identifiers, which may be pointers to locations in storage for the
bit vectors for the term. If an explicit mapping is used for a
term, identifying the bit vector locations for the term when
indexing a document or performing a query involves looking up the
mapping for the term and retrieving the specific bit vector
locations identified by the mapping.
[0184] While it's possible to provide explicit mappings for all
terms, especially for search indexes for a smaller set of documents
with a smaller number of terms, it may be impractical to include
explicit mappings for all terms in larger search indexes containing
a large number of terms. Accordingly, another approach in some
configurations is to not include explicit mappings for at least
some terms, but instead use an "ad hoc" approach, in which
algorithms are employed to derive the bit vector locations for
terms. In accordance with some aspects of the technology described
herein, algorithms are provided for each band that are used to
determine bit vector locations based on a derivative of the term's
hash. For instance, if the bit vector configuration for a band
specifies three bit vectors, three algorithms may be provided that
are each a function of the hash of a term. Accordingly, to find bit
vector locations using an ad hoc approach for a term, the term's
band is determined based on characteristics of the term, and
algorithms specified for that band then may be employed to
determine bit vector locations for the term. The algorithms for
each band may be stored in the band table, term table, or some
other data structure. The algorithms for a band may simply be
different hash functions that are uncorrelated in the sense that if
the various hash functions are applied to the same input value
(i.e., the same term), there's a high probability a different
result will be returned for each of the hash functions.
[0185] Some configurations employ both explicit mappings and an ad
hoc approach for different terms in the search index. In
particular, explicit mappings may be used for the most common terms
in the search index, while an ad hoc approach may be used for the
remaining terms.
[0186] Turning to FIG. 25, a flow diagram is provided that
illustrates a method 2500 for determining bit vector storage
locations using explicit mappings and ad hoc information.
Initially, as shown at block 2502, a search query is received. The
method 2500 may be performed at least partially, for instance,
using the matcher 4404 of FIG. 44. One or more terms are identified
from the search query, as shown at block 2504
[0187] One or more data structures are accessed at block 2506 to
determine the storage locations of bit vectors for a term
identified at block 2504. The data structures may include the band
table and/or term table discussed above. In particular, the data
structures provide explicit mappings for some terms in which the
storage locations of bit vectors are explicitly identified for
those terms. The data structures also store ad hoc information for
deriving the storage location of bit vectors for other terms for
which explicit mappings are not provided. The ad hoc information
may provide mapping algorithms for determining the storage
locations of bit vectors. Different mapping algorithms may be
provided for different bands. As such, the ad hoc information may
map term characteristics to mapping algorithms. In other words,
different sets of term characteristics may be mapped to different
mapping algorithms.
[0188] A determination is made at block 2508 regarding whether
explicit mapping information or ad hoc information will be used to
identify the storage locations of bit vectors for the term
identified at block 2504. For instance, a term table may store
explicit mappings. The term table may be checked to see if it
includes the term. If so, the explicitly provided bit vector
storage locations are identified, as shown at block 2510. If
explicit mapping information is not available, ad hoc information
is employed to derive the bit vector storage locations for the
term, as shown at block 2512. This may involve determining term
characteristics for the term and looking up mapping algorithms for
those term characteristics. For instance, the mapping algorithms
may be stored by a band table in which different mapping algorithms
are set forth for different bands. The band for the term may be
determined based on the term characteristics and mapping algorithms
identified for that band. The mapping algorithms are then used to
derive the bit vector storage locations.
[0189] Bit vectors for the term are accessed, as shown at block
2514. If multiple terms are identified at block 2504, the process
of accessing bit vectors at blocks 2506, 2508, 2510, 2512, and 2514
is performed for each term. Bit vectors accessed are then
intersected to identify matching documents for the search query, as
shown at block 2516.
[0190] The term table may be populated with data in a number of
ways in accordance with various aspects of the technology described
herein. For instance, given a static corpus of documents that is
being indexed, some statistics on the frequencies of terms in those
documents could be determined by scanning the documents and
counting up the terms and based on that information build a term
table for that set of documents. However, the characteristics of
documents on the web in aggregate are fairly stable so a random
number of documents (e.g., 10 million documents) could be selected,
term frequencies could be computed for that set of documents, a
term table could be built that would fairly optimally store the
postings from those documents and then you could use that term
table for other documents.
[0191] When generating the explicit mappings for a term table, some
configurations are directed to maintaining bit vector density
(i.e., percentage of bits set in each bit vector) around some
desired bit density. For instance, an algorithm could be used to
generate explicit mappings that selects bit vectors for terms to
achieve that desired bit density based on frequencies of terms in
documents. The bit density of the bit vectors doesn't need to be
exactly equal to the desired bit density; but instead, the approach
is to attempt to stay near the desired bit density (e.g., some bit
vectors may have slightly higher bit densities and some bit vectors
may have slightly lower bit densities)
Row Trimming/Augmentation
[0192] When designing and building a search system using bit
vectors, the amount of information stored the search index may be
based on worst case queries. There are some queries that can be
processed with only a small amount of indexed information. On the
other end of the spectrum, there are queries that require large
amounts information to be stored to handle the queries well.
[0193] Interestingly, the hardest queries to handle from an
information storage perspective are queries consisting of a single
word in which the only bit vector information available for the
query are the bit vectors for that single word. In contrast, a
query that is a conjunction of multiple words (e.g., 3 or 4 words,
which is more typical), each additional word requires less
information to handle the query. If a query has a large number of
words and the system retrieves all bit vectors for each word,
performing the query may entail bringing in a massive amount of
information and the query may become inefficient. However, not all
of that information is needed to perform the query well.
[0194] Some aspects of the technology described herein employ what
is referred to herein as row trimming and row augmentation, which
is directed to using less (trimming) or more (augmentation) of the
available bit vectors for each term for a query when performing
matching for the query. For instance, suppose a query includes
three words that each have an IDF of four such that each word is
stored in five bit vectors (based on an example rule of thumb in
which the number of row intersections for a word corresponds to its
IDF). Accordingly, there are 15 bit vectors available to intersect
for this query. However, 14 bit vector intersections of the 15 bit
vectors are more than required.
[0195] Instead of using all the bit vectors available for the three
words, a portion of the available bit vectors may be used.
Generally, the IDF of the query may be used to determine the number
of bit vectors to intersect. For instance, in the previous example
of a query with three words, a target false positive rate of 1 in
10,000 would require only four intersections of five bit vectors.
The five bit vectors may be selected from the 15 bit vectors
available for the three words (e.g., two bit vectors from a first
word; two bit vectors from a second word; and one bit vector from a
third word).
[0196] Bit vectors for terms may be spread out across different
types of storage media (e.g., DDR RAM, SSD, and HDD). For more
typical queries having multiple terms, row trimming allows only the
bit vectors in the faster storage media (e.g., DDR RAM and/or SSD)
to be retrieved. For queries requiring more information for a term
(e.g., a single term query), row augmentation retrieves the bit
vectors from the slower storage media (e.g., SSD or HDD) in
addition to the rows in the faster storage media. Because queries
requiring more information are typically rarer, it's acceptable to
store the additional bit vectors in the slower storage media. For
example, suppose a term has seven bit vectors, with five bit
vectors stored in DDR RAM and two bit vectors in HDD. Some queries
may only require two or three of the bit vectors located in DDR
RAM. More typical queries may require four or five of the bit
vectors located in DDR RAM. Some rare queries may require all seven
bit vectors.
[0197] One consideration when constructing the term to row mappings
(e.g., in a term table) is determining how many bit vectors to use
for each term and on which storage media to store the bit vectors
for each term. The determination of the number of bit vectors for a
term may be based on the term's frequency in a corpus. Rarer terms
require more bit vectors. The determination of which storage media
to store the bit vectors for a term may be based on the term's
frequency in queries. Terms that appear less often in queries can
reside on slower media. This weighs the cost of storing bit vectors
for a term against the likelihood/frequency of using the term's bit
vectors when processing queries. Generally, the number of bit
vectors and their locations on various storage media may be encoded
in the band table.
[0198] One consideration when a query is received is determining
how many bit vectors to include for each term in the matcher plan.
The determination may be treated as an optimization problem that
weighs the benefit of reduced noise from additional bit vectors
with the cost of retrieving those bit vectors from slower storage
media. The benefit of the additional bit vectors may be quantified
by a relevance metric (e.g., false positive rate; error rate;
fix-up cost; or other metric).
[0199] Turning to FIG. 26, a flow diagram is provided illustrating
a method 2600 for row trimming/augmentation for a search query. The
method 2600 may be performed at least partially using, for
instance, the matcher 4404 of FIG. 44. Initially, as shown at block
2602, a search query is received. One or more terms are identified
from the search query, as shown at block 2604.
[0200] A number of bit vectors to use for each term is determined,
as shown at block 2606. As described above, this may be based on
factors such as the benefit of noise reduction from additional bit
vectors and a cost of retrieving additional bit vectors (which may
consider the type of storage media at which the bit vectors are
stored). The determination may employ a heuristic and/or may be
based on intersecting an initial number of bit vectors to estimate
a number or percentage of matching documents and then re-running
the intersections using a different number of bit vectors that is
based on the estimate. In some instances, a priority may be set to
the available bit vectors, and bit vectors may be selected in
accordance with that priority. The bit vectors are intersected at
block 2608 to identify matching documents.
[0201] Another approach would be to dynamically adjust the number
of bit vectors based on bit densities observed while performing bit
vector intersection (although this approach may not provide query
stability). Such an approach is different from determining an
initial number of bit vectors and then re-running using a new
number of bit vectors, in that the matching process is not re-run.
Instead, bit vectors are added or removed while the matching
process continues. This approach is shown in FIG. 27, which
provides a flow diagram illustrating another method 2700 for row
trimming/augmentation for a search query. The method 2700 may be
performed at least partially using, for instance, the matcher 4404
of FIG. 44. Initially, as shown at block 2702, a search query is
received. One or more terms are identified from the search query,
as shown at block 2704.
[0202] An initial number of bit vectors is determined for each
term, as shown at block 2706. This initial number may be
determined, for instance, using a heuristic. The determination may
consider how many matching documents are expected to be returned, a
relevance metric, and/or a cost of retrieving bit vectors from
storage.
[0203] The initial number of bit vectors is used to begin a
matching process by intersecting the bit vectors, as shown at block
2708. While the matching process is performed, the number of bit
vectors being used is adjusted, as shown at block 2710. This may
include adding additional bit vectors and/or removing bit vectors.
The number of bit vectors may be adjusted any number of times
during the matching process. The adjustment may be based on
different considerations, such as the number or percentage of
matching documents being returned and/or the cost/cost savings of
retrieving more/fewer bit vectors from storage. In some instances,
a priority may be assigned to the available bit vectors, and bit
vectors may be added or removed in accordance with that
priority.
Updating Search Index
[0204] Search indexes need to be updated as new documents become
available and previously indexed documents are modified or become
stale (and therefore may be removed). Updating a search index built
using posting listings has traditionally been problematic. Posting
lists are typically sorted (e.g., by document ID or static rank),
which makes it hard to add and remove documents. Adding a document
to a posting list involves determining the location in the posting
list to add the document ID then moving other document IDs to allow
for the addition of the document ID. If a document needs to be
removed from a posting list, the document ID is removed and other
document IDs then need to be moved based on the removal. Moving
document IDs based on additions and removals impacts skip lists
and/or other mechanisms used by the search system, and the skip
lists and/or other mechanisms need to be updated based on the
movement of the document IDs. As a result, updating a posting
list-based search index may require bringing a server offline,
rebuilding the search index, and then bringing the server back
online. The process of rebuilding the search index may be time
consuming if the server indexes a large collection of documents,
resulting in the server being offline for a long period of time. If
the length of time is sufficiently long, the search index may be
updated less frequently, causing the search index to become
stale.
[0205] An advantage of using a bit vector-based search index, such
as the bit vector search index 4410 of FIG. 44, is that the search
index may be incrementally updated without the need to take a
server down for any period of time, which is the case of some
search systems. Because the bit vectors may all be a constant width
in the context of representing the same number of documents such
that the space for new documents is pre-allocated, adding and
removing documents may be performed by simply setting and clearing
bits, as will be discussed in further detail below.
[0206] In contrast to posting lists, the bit vectors do not suffer
from the problem associated with maintaining documents in sorted
order. There is no need to shift document IDs or to update pointers
as may be required when updating posting lists. Adding or removing
documents may be performed even while the system is running. If a
search query is received when performing an update, one of two
outcomes is possible depending on the progress of the update. The
first possible outcome is the set of matches that would have been
identified prior to the update. That is, the search query was
performed before the bits were changed in a manner that would
impact the results. The second possible outcome is the results that
would have been identified after the update. That is, the search
query was performed after the bits were sufficiently changed to
impact the outcome. There is no point in time when any other result
set could be provided. Because updating the bit vectors may be done
quickly with minimal or no downtime, the data center design is
simplified since it does not need to account for substantial
downtime and there is no concern with the search index becoming
stale due to infrequent updates.
[0207] Turning to FIG. 28, a flow diagram is provided that
illustrates a method 2800 for adding a document to a bit
vector-based search index. The method 2800 may be performed, for
instance, by the indexer 4418 to update the bit vector search index
4410 in the search system 4400 shown in FIG. 44. As shown at block
2802, terms in a document are identified. The location (e.g., body,
non-body, meta) of each term may also be identified. As shown at
block 2804, a column to add the document is selected.
[0208] By way of example to illustrate identification of a column,
FIG. 29 illustrates a simplified search index 2900 with a
collection of bit vectors of varying length. The highlighted
portion 2902 is a column allocated for indexing a particular
document, including the bits in each bit vector that corresponds to
that document. As can be understood, the bits of the column in the
short row bit vectors are shared with other documents.
[0209] In some configurations, the bit vectors in a search index
may include a number of "empty" columns to allow for the addition
of documents. The columns are empty in the sense of having of their
bits set to zero. Note an empty column may have bits set in some
short row bit vectors based on the presence of other documents
sharing those bits.
[0210] The bit vectors corresponding to terms found in the document
are identified, as show at block 2806. The bits in each of the
identified bit vectors corresponding to the column selected for the
document are identified, as shown at block 2808, and the identified
bits are set, as shown at block 2810 (i.e., by setting each of the
bits to "1").
[0211] With reference now to FIG. 30, a flow diagram is provided
that illustrates a method 3000 for removing a document. The method
3000 may be performed at least partially using, for instance, the
indexer 4418 of FIG. 44. As shown at block 3002, a column
corresponding to a document to be removed is identified. As noted
above, a column refers to the bits in each bit vector corresponding
to a particular document. By way of example, FIG. 31A illustrates a
simplified search index 3100 with a set of bit vectors of varying
length (the shorter bit vectors being stretched out to show
corresponding bits). The column (i.e., collection of bits)
corresponding to a document to be removed are highlighted by the
area 3102.
[0212] Each of the bits in the identified column is set to zero, as
shown at block 3004. Setting all bits in the column to zero is
represented in FIG. 31B. Because the bits in the shorter bit
vectors are shared by other documents, some of which will remain in
the index, the bits in the shorter bit vectors may need to be
restored for those documents. Accordingly, the collection of
documents sharing bits in the shorter bit vectors are identified,
as shown at block 3006. These are the documents corresponding to
the columns 3104, 3106, 3108, 3110, 3112, 3114, 3116 shown in FIG.
31C. The bits in the shorter bit vectors corresponding to the terms
contained in those identified documents are reset, as shown at
block 3008. This may be done, for instance, by identifying the bit
vectors corresponding to terms contained in the documents,
identifying the bits in those bit vectors corresponding to the
documents, and setting those bits (similar to the approach for
adding documents discussed above with reference to FIG. 28). FIG.
31D illustrates bits that have been reset in the search index 3100
based on the documents corresponding to columns 3104, 3106, 3108,
3110, 3112, 3114, 3116.
[0213] The above approach for removing a particular document may be
an expensive operation since it requires the documents sharing bits
with the document removed to be re-indexed. Therefore, the approach
may be employed in limited circumstances, for instance, when a
document needs to be removed from the search index for legal
reasons.
[0214] Another approach for removing documents from the search
index that would remove the complications of having to re-index
documents sharing bits with removed documents is to remove
documents in batches. In that way, all documents sharing bits are
removed at the same time by setting all the bits to zero and no
documents would need to be re-indexed. For instance, an expiration
approach could be employed in which a policy dictates that
documents are removed every so often (e.g., every 24 hours, weekly,
monthly, etc.). According to the policy, all documents older than
the set time threshold would be removed by setting the bits for
those documents to zero. The timing threshold may coincide with how
frequently documents are indexed. By way of example to illustrate,
documents may be indexed every 24 hours. As such, documents that
were indexed 24 hours ago would be removed from the search index
(i.e., by setting the bits to zero in the columns for the
documents) around the same time the documents are crawled again and
re-indexed. When a document is crawled again, it may be indexed
using the same column previously employed. However, a simpler
approach may be to simply zero out the bits in the previous column
and index the document in a new column in the search index. This
facilitates removing documents in batches as documents are added to
contiguous locations in the search index based on when they're
crawled.
[0215] Another approach to removing documents from the search index
is to not truly remove the indexed information but instead to
prevent certain documents from being returned in response to search
queries. In particular, a long bit vector may be stored in the
search index and intersected during matching for all search
queries. The bits in the long bit vector may be initially set to
one, and if a document is to be removed, the bit for that document
is set to zero. As such, when a search query is received and that
long bit vector is intersected, any document with a bit set to zero
is effectively removed. While this approach provides a relatively
simple way to "remove" documents, it has a cost because the
"removed" documents are taking up space in the search index.
However, this may be acceptable for random access deletions (e.g.,
need to remove a document for legal reasons) because the instances
of random access deletions may be relatively rare.
[0216] When the index is stored entirely in RAM, updates to the
index is relatively straight forward. For instance, if an
expiration policy is employed, the search index in RAM may
conceptually just be considered as a 2D array of bits in which
documents are added to the right-hand side and documents are
removed on the left-hand side. However, larger search indexes may
not practically fit entirely in RAM, and other storage devices,
such as SSDs and/or HDDs, may be employed to store portions of the
search index. In particular, SSDs and HDDs have larger storage
capacities and cost relatively less. However, SSDs and HDDs are
generally slower than RAM both in the limit on the number of
requests per second each can handle (i.e., IOPS--input/output
operations per second) and the rate at which data can be
transferred (i.e., throughput measured, for instance, in bytes per
second or MB per second).
[0217] Performance considerations for incremental index update
include, but are not limited to, the cost of adding columns to a
two-dimensional array and the inefficiencies due the block oriented
nature of data storage devices like RAM, SSD, and HDD. By way of
example to illustrate such considerations, FIG. 32A shows a
4.times.4 array arranged in row-major order. When an array is laid
out in row-major order, consecutive column positions within a row
reside in consecutive storage locations. As an example, columns A-D
reside in positions 0-3 in row 1 and positions 4-7 in row 2. In
accordance with configurations described herein, postings are
arranged in row-major order where columns correspond to sets of
documents and rows correspond to sets of terms. This arrangement is
used to optimize the speed of row scans during query
processing.
[0218] During the course of document ingestion, it may be necessary
to add another column to the array. FIG. 32B shows the layout of
data from the original array after adding a fifth column. In order
to maintain a row-major layout, it was necessary to move the data
that was originally in storage positions 4-15. As an example,
consider the position B2. In the original 4.times.4 array in FIG.
32A, position B2 corresponded to storage location 5. In the new
4.times.5 array in FIG. 32B, position B2 corresponds to storage
location 6.
[0219] Because of these data moves, the amount of work to add a
single column is on the order of the amount of work to copy the
entire array. One way to avoid the costs associated with adding
columns is to start with a larger array that reserves space for
additional columns. FIG. 33A shows an example of such an array. In
this particular example, the array has space for 6 columns, but
only two are in use. FIG. 33B shows that adding a third column
involves only writing to the storage locations associated with that
column. Other storage locations remain untouched. After adding
another three columns, the array will become full, as shown in FIG.
33C.
[0220] At this point the array can be copied to a larger buffer as
shown in FIG. 34A. Alternatively, a new buffer can be started, as
shown in FIG. 34B. Copying the array, as shown in FIG. 34A, is
expensive, but has the advantage that each row maps to a contiguous
block of storage which can be scanned efficiently. Starting a new
buffer, as shown in FIG. 34B, is inexpensive, but has the
disadvantage that each row now maps to a pair of blocks. The
storage within each block is contiguous, but the blocks themselves
are not in adjacent storage locations. Some devices, like SSD and
HDD, incur a significant setup cost for each block of contiguous
storage accessed. For these devices, the arrangement in FIG. 34B
would incur twice the setup cost as the arrangement in FIG.
34A.
[0221] In order to provide acceptable performance while reading
rows, the number of blocks of storage in the index needs to be
limited. At the same time, to provide acceptable performance while
ingesting documents, the number of times a block is copied needs to
be limited. Configurations described herein use a hierarchy of
arrays to minimize the number of block copy operations while
enforcing a limit on the number of blocks that make up a row. As an
example, some configurations can employ space for two small arrays,
two medium arrays, and two large arrays, as shown in FIG. 35A. In
this example, small arrays hold half as many columns as medium
arrays. Large arrays hold five times as many columns as small
arrays.
[0222] Initially, the system is empty, as shown in FIG. 35A. As
documents arrive, they are indexed into a newly created small array
as shown in FIG. 35B. As in FIG. 33A, the small array consists of a
set of columns containing documents that have already been indexed
and a set of columns reserved for documents that will be indexed in
the future. At this point, a row can be accessed with a single
block read.
[0223] At some point, the first small array becomes full and a new
small array is created to accept additional documents, as shown in
FIG. 35C. At this point, accessing a row requires two block read
operations. Eventually the second small array becomes full as shown
in FIG. 35D. At this point, a medium sized array is created and
initialized with a copy of the contents of the two small arrays as
shown in FIG. 35E. The two smaller arrays are then cleared and
document ingestion continues in the first small array. In this
configuration, a row access requires two block read operations.
Eventually the small arrays will fill up again and a second medium
block will be created, as shown in FIG. 35F. At this point, a row
access requires three block read operations. At some point, both
small arrays will become full again, but this time both medium
arrays will be full as well, as shown in FIG. 35G. In this
situation, there are no medium arrays available to hold the
contents of the small arrays. A row access now requires four block
read operations. At this point, a new large array is created and
initialized with the contents of the two small arrays and the two
medium arrays. The small and medium arrays are then cleared and
ingestion continues in the first small array as shown in FIG. 35H.
A row access now requires two block read operations.
[0224] Data storage devices typically provide read/write access to
data at a granularity greater than a single bit. Bits on these
devices are grouped into blocks which represent the smallest amount
of data that can be read or written in a single operation. As an
example, the DDR3 memory protocol arranges data into blocks of 512
bits. Reading a single bit from DDR3 memory requires a reading of
all 512 bits in the block containing the bit. Likewise, writing a
single bit requires writing all 512 bits in the block. SSD and HDD
have even larger block sizes. For example, a typical SSD may
arrange data into blocks of 4,096 bytes, or 32,768 bits. Reading or
writing a single bit on such an SSD would involve reading or
writing 32,768 bits. A typical HDD block is even larger.
[0225] As noted above, configurations described herein arrange
posting data as a two-dimensional array of bits, where rows
correspond to sets of terms and columns correspond to sets of
documents. The 2D array of bits is laid out in row major order.
That is, the bits within a single row occupy consecutive storage
locations, and the rows which make up the array occupy consecutive
storage locations. The consequence of this layout is that
operations on a single row involve access to a sequence of
consecutive storage locations, while operations on column require
access to a sequence of storage locations that are not consecutive.
The act of adding, updating, or removing a document involves
writing to bits within a single column, and therefore requires
access to non-consecutive storage locations.
[0226] This operation is inefficient because reading or writing a
single bit involves reading or writing a complete block of bits. In
the case of updates to DDR memory, reading or writing a single bit
involves an operation on 512 bits. Therefore, 511/512th of the
storage device throughput is wasted, compared to an operation
reading or writing 512 consecutive bits. This inefficiency is
acceptable for postings stored in DDR memory because document
ingestion rates are fairly low, relative to the high throughput
rate of the DDR memory.
[0227] When postings are placed on SSD or HDD, however, the
inefficiencies due to block access become unacceptable for two
reasons. The first reason is that SSD and HDD typically have much
larger block sizes. For instance, SSD may use blocks of 4 Kb (32 k
bits) and HDD may use blocks of 16 Kb (132 k bits). These blocks
are 64 and 256 times larger, respectively, than the typical DDR3
blocks. The consequence is that reading or writing a single bit
stored on SSD or HDD is 64 to 256 times less efficient than reading
or writing a single bit stored in DDR3 memory. The second reason is
that the time to read or write a block on SSD or HDD is much
greater than reading or writing a block of DDR3 memory. For
example, a typical SSD operation may take 20 ms while a typical
DDR3 operation may take 10 ns. In other words, reading or writing a
block of SSD may be 2 million times slower than accessing a block
of data in DDR3 memory. HDD is even slower.
[0228] With an index arranged as a hierarchy of arrays as shown in
FIGS. 35A-35H, it is possible to mitigate the inefficiencies
associated with offline storage devices by placing the small arrays
in DDR storage and the medium and large arrays on SSD and HDD, as
shown in FIG. 36. The reason this works is that individual column
write operations only happen in the smallest arrays. Since the
smallest arrays are stored in DDR, the costs for the column writes
are low. The larger arrays are only initialized by copying the
entire contents of a set of smaller arrays. These large copy
operations are efficient for offline storage devices. In some
configurations (such as in the examples of FIGS. 35A-35H), data may
be written from a collection of arrays to a larger-sized array
(e.g., small to medium or medium to large) such that the data
written to the larger-sized array fills that array, limiting the
number of writes to the larger-sized array.
[0229] Each of the arrays can be referred to herein as an
accumulation buffer as each array serves to accumulate documents
until some point is reached and the contents are then written to a
larger array. Turning now to FIG. 37, a flow diagram is provided
that illustrates a method 3700 for using accumulation buffers to
index documents in a bit vector search index. The method 3700 may
be performed at least partially using, for instance, the indexer
4418 of FIG. 44. Initially, documents are indexed in an
accumulation buffer storage device, as shown at block 3702. The
accumulation buffer storage device stores document information as
bit vectors in which each bit vector comprises an array of bits
with each bit indicating whether at least one of one or more
documents contain at least one of one or more terms corresponding
to the bit vector. In the instance in which the accumulation buffer
storage device is an initial storage device, each document may be
indexed in the accumulation buffer storage device one at a time by
setting bits for each document. For instance, bits for a document
may be set in the accumulation buffer storage device after crawling
the document. In other instances, the accumulation buffer storage
device at which documents are indexed at block 3702 may be preceded
by one or more previous accumulation buffers. In such instances,
the documents may be collectively indexed in the accumulation
buffer storage device based on the bits set in a previous
accumulation buffer.
[0230] A determination is made at block 3704 regarding whether a
threshold has been satisfied. If not, the process of indexing
documents in the accumulation buffer storage device is continued as
represented by the return to block 3702. Alternatively, if the
threshold has been satisfied, indexed document information from the
accumulation buffer storage device is collectively indexed in a
subsequent storage device, as shown at block 3706. As can be
understood, when the subsequent storage device is larger than the
accumulation buffer storage device, data may be moved from
consecutive bits in the accumulation buffer storage device to
non-consecutive bits in the subsequent storage device.
[0231] Different thresholds maybe employed in various
configurations. In some instances, the threshold is a certain
number of documents, such that when the certain number of documents
have been indexed in the accumulation buffer storage device, the
threshold is satisfied and information is indexed from the
accumulation buffer storage device to the final storage device. In
other instances, the threshold is a certain period of time (e.g.,
an hour, a day, etc.) such that when the time period has passed,
the information is indexed from the accumulation buffer storage
device to the final storage device. In still further instances, the
threshold may be a certain storage amount (e.g., the storage
capacity set for the accumulation buffer storage device or a
collection of accumulation buffer storage devices), such that when
the storage amount has been met, the information is indexed from
the accumulation buffer storage device to the final storage
device.
[0232] As shown at block 3708, a determination is made regarding
whether the subsequent storage device is full. If not, the process
of indexing documents in the accumulation buffer storage device
(e.g., by flushing the accumulation buffer storage device and
indexing new documents) until a threshold is satisfied and indexing
information from the accumulation buffer storage device to the
subsequent storage device may be repeated. This process may be
continued until the subsequent storage device is full, at which
time the process ends as shown at block 3710. Other thresholds
besides whether the final storage device is full may be employed in
determining whether to repeat the process. For instance, a
time-based threshold could be used instead (e.g., the final storage
device may be configured to hold a day's worth of documents) or a
document threshold (e.g., the final storage device may be
configured to hold a threshold number of documents).
[0233] It should be understood, that the number and size of
accumulation buffers may be configurable based on design goals.
Generally, more accumulation buffers may be desirable to a certain
point where other costs make it less desirable to have additional
accumulation buffers. In particular, accumulation buffers may be
used to serve search queries (i.e., a search query would be served
based on documents indexed in not only a final storage device (i.e.
large storage device) but also the documents currently stored in
the accumulation buffers that have not yet been provided to the
final storage device). As such, more accumulation buffers may slow
down query processing speed as each accumulation buffer is accessed
to serve the search query. Depending on design goals, an optimal
number of accumulation buffers may be selected. For example, if the
search index will experience a high volume of queries but data is
not updated too often, the optimal design may be fewer accumulation
buffers. As another example, if the search index will experience
infrequent search queries but data is updated often, the optimal
design may be more accumulation buffers. Additionally, SSD are
susceptible to burnout after a certain number of writes. Therefore,
the number of accumulation buffers on SSD will affect burnout, and
the burnout rate of SSDs may be taken into consideration when
selecting the number of SSD accumulation buffers to employ in the
design.
Preliminary Ranker Algorithm
[0234] As discussed herein, a search system may use a matcher, such
as the matcher 4404 of FIG. 44, to initially identify a group of
matching documents for a search query. As this group of documents
is, in most cases, too large to be returned as a set of search
results, one or more rankers may be utilized to further narrow the
group of documents so that only the most relevant documents are
returned in response to the search query. In one configuration, at
least two rankers are used, including a preliminary ranker, such as
preliminary ranker 4422 of FIG. 44. While the preliminary ranker is
able to closely approximate what subsequent rankers, such as the
final ranker 4426 of FIG. 44, would do in terms of scoring and
ranking documents, the preliminary ranker is less expensive to
operate. For example, the preliminary ranker, in one aspect of the
technology described herein, eliminates all documents from
consideration for the subsequent rankers that the subsequent
rankers would also eliminate. As such, the algorithm used by the
preliminary ranker is designed to eliminate (e.g., assign low
scores to) all documents that would also be eliminated by the
algorithms used by subsequent rankers, such as final ranker 4426 of
FIG. 44. This allows for the set of candidate documents at the
preliminary ranker to be significantly reduced without eliminating
a document that is particularly relevant to the query and that
should be included in a set of candidate documents at the final or
other subsequent ranker.
[0235] Referring now to FIG. 38, an exemplary system 3800 is
illustrated for carrying out aspects of the technology described
herein. A matcher 3802 (which may correspond to the matcher 4404 of
FIG. 44), a score table server 3804, and a preliminary ranker 3610
(which may correspond to the preliminary ranker 4422 of FIG. 44)
are provided, and may communicate by way of a network 3608. The
matcher 3802 has been previously described herein, and thus will
not be described in relation to system 3800. Identifications of
documents found to be relevant by the matcher 3802 are returned to
the preliminary ranker 3810. For each document indicated as being
potentially relevant to a particular search query, the score table
server 3804 accesses a score table associated with each document.
In one configuration, the score tables are stored in a score table
data store, such as data store 3806.
[0236] The preliminary ranker 3810 has many functions, as described
in more detail herein. For instance, the preliminary ranker 3810
comprises, among other components not shown in FIG. 38, a score
table building component 3812, a score table lookup component 3814,
a scoring component 3816, a key comparison component 3818, and a
click table lookup component 3820. The functionality of each of
these components will be described in more detail below.
[0237] While traditional rankers may utilize a payload of data
associated with each item in posting lists to score and rank
documents, aspects of the technology described herein instead use
tables with pre-computed data. Posting lists may utilize inverted
indices that could represent an entire corpus of documents. A
posting list, for example, may first be arranged by document, and
then by occurrence of each term in the document. The list may also
include a pointer that can be used to move from a first occurrence
of a term to subsequent occurrences of that same term. While
posting lists may assist with reducing the number of candidate
documents, they also consume a great deal of memory, and are slower
to use than the score tables described herein.
[0238] Instead of using a posting list, as described above, some
configurations utilize hash tables, also termed score tables. In
one aspect, each document has its own score table that comprises
pre-computed data, such as frequency data. As mentioned, these
score tables may be stored in data store 3806. Score tables may
also comprise other data that has been pre-computed. In regards to
the frequency, the frequency may be pre-computed, but may be stored
in the score table not as the actual frequency of a term in a
document, but as, for example, an IDF. An IDF increases
proportionally to the number of times a term appears in the
document, but is offset by the frequency of the word in the corpus.
Stated in a different way, the value stored in the table may
reflect the frequency of a particular term in a document in
relation to the relative infrequency of that term in the corpus.
Other ways of representing the pre-computed frequency of terms in
the score table are also contemplated. As such, the data stored in
the score table in relation to the frequency of a term may be
indicative of the frequency of the term in the document, but may be
stored in such a way as to require some type of computation to
determine the actual frequency. The algorithm used by the
preliminary ranker may use data indicative of the frequency in its
computation of a score for each document, and thus may not need to
compute the actual frequency of the term. Even further, for
efficiency purposes, such as to reduce the memory required, the
frequency data may be clipped at a maximum frequency for the terms
in the score tables so that frequency data can be represented with
less bits in the score tables.
[0239] As mentioned, each document may have an associated score
table that stores data for pre-computed components that are used to
score and rank documents by the preliminary ranker. In order to
produce an efficient ranker, the score table building component
3812 may not include all terms in a document in the score table.
For instance, data for only those terms that occur more than once
in the body of a particular document may be stored in that
document's score table. Approximately 85% of terms in a document
may be found just once in the body of the document, so eliminating
the pre-computation of various components associated with these
terms saves memory, and makes the preliminary ranker operate much
faster than it otherwise would. As a result, both the terms that
appear only once in the body of a document and the terms that do
not appear at all in a document may be treated the same, and thus
may be given the same score, as the preliminary ranker may not be
able to distinguish between these. Because the system knows that
the terms occurring only once in the body of a document are not
included in the score table for each document, the system, in one
configuration, treats all terms not found in a particular score
table as occurring once in the body. This means that terms not
contained in a document would be treated as occurring once in the
body. This is acceptable since it will not significantly impact the
ranking. Terms from other locations (e.g., non-body, and metawords)
may be scored higher and information stored even if the terms
appear only once in these other locations.
[0240] By way of example to illustrate, if a particular search
query includes both terms "cat" and "dog," a document may be
returned that was found to have the term "cat." The preliminary
ranker may access the score table for that particular document to
find that "dog" is not listed in the score table, and may assume
that "dog" is only mentioned once in the body of the document. In
this scenario, the preliminary ranker may give the document a score
of "1" instead of "0," which would typically be given to a document
in which the term does not occur at all. As such, in one
configuration, no documents are given scores of "0" for a
particular term not being found in a score table.
[0241] While the frequency of each term in a document has been
discussed, other pre-computed data may also be stored in the score
tables. For instance, an indication of where each term occurs in a
document may be encoded in the score tables. For instance, in one
type of a document, a term could be located in the title stream,
body stream, anchor stream, URL stream, etc. A term that is located
in the title of a document may indicate, for example, that the
document has a good chance of being relevant to that term, and thus
to the user's intent associated with the search query. Further, a
particular term occurring multiple times in a single paragraph or
in a particular section of a document may indicate particular
relevancy of that document to the search query.
[0242] In addition to the pre-computed components discussed above,
such as the frequency of the term in the document and in which
portion of the document the term is located, one or more real-time
components may also be taken into account when a final score is
computed for a document in relation to a search query, such as by
the scoring component 3616. Real-time components are those that are
computed once a search query is entered and received, as they
cannot generally be pre-computed. For example, the location of a
particular term in the search query is not able to be computed
until runtime, as the query is not known until that time. Further,
how well the geographic local of a document matches the geographic
local of the origin of the query cannot be determined until
runtime, and as such, is calculated in real time. Another example
is how well the language of a document matches the language of the
query. This also would not be calculated until a search query is
entered, and the preliminary ranker runs an algorithm to determine
how relevant a set of documents is to the search query.
[0243] The final score of a document in relation to the search
query, as computed by the scoring component 3816, may be dependent
upon both of one or more pre-computed components and one or more
real-time components. For instance, each component, whether
pre-computed or not, may be assigned an individual score by the
algorithm used by the preliminary ranker. The algorithm, such as
the scoring component 3816, then considers the individual scores to
compute a final score for each document in relation to a particular
search query. The final score may be used to rank documents, or
otherwise to eliminate some documents from consideration by a
subsequent ranker. In one configuration, the final score is a
number that indicates how well a particular document corresponds to
the search query.
[0244] A click table may also be used by the preliminary ranker in
determining a score for each document. A click table may function
much the same as the score table as described above. Data is stored
in slots of a click table for each term of a document. In one
configuration, all terms found in a document are included in a
click table, but in another configuration, only those terms that
occur more than once are included in a click table. For each term,
the click table stores data that indicates how often that document
is selected by users who submit the same or similar search queries.
How often a particular document is selected by other users who
submit the same or similar search queries can be a valuable
indicator as to whether or not that document should be considered
relevant for the present search query. As such, a click table may
be accessed by, for example, the click table lookup component 3820,
as one component that can contribute to a final score of a document
for a particular search query.
[0245] FIG. 39 illustrates a flow diagram of a method 3900, for
instance using the preliminary ranker 3810 of FIG. 38, to score a
plurality of documents based on relevancy to a search query.
Initially at block 3902, a table is accessed that is associated
with a document found to be potentially relevant to at least a
portion of a received search query. The table may store data used
to derive a frequency of each term of a subset of terms in the
document. In one configuration, each term in the subset of terms
occurs more than once in the document. In one instance, less than
half of all terms in a document are included in the subset of
terms. The document may be one of a plurality of documents that
have been found by, for instance, matcher 4404 of FIG. 44, to have
a potential of being relevant to the search query based on a
keyword match. At block 3904, the frequency of at least one term
corresponding to the search query is determined. In one
configuration, the determination of the frequency may simply refer
to the frequency data from the table being accessed and retrieved.
How the data is processed is dependent upon the algorithm. For
instance, the algorithm, may need the data in the table to be
transformed to a different representation of a frequency, such as
from an IDF to just the frequency of the term in the document.
Alternatively, the algorithm may use the IDF in its calculation of
the score for the document. As previously described, data
indicative of the frequency may be pre-computed, and may be stored
in the table, and as such, at block 3904, the data in the table is
used to determine a frequency by an algorithm used by the
preliminary ranker. This frequency data stored in the table may
provide an indication of not just the frequency of a term in the
document, but a frequency of a term in the document in relation to
a relative infrequency of that term in a corpus of documents.
[0246] At block 3906, a score of the document in relation to the
search query is computed. This is based on, at least, the frequency
of the at least one term in the document and other data associated
with the document and terms of the search query. The frequency is a
pre-computed component. Other pre-computed components include a
location of the term in the document, such as whether the term is
found in the title, body, abstract, anchor, URL, etc. At least a
portion of the score may be based on one or more real-time
components that are computed in real-time, such as at runtime.
These may include, for example, a location of at least one term in
the search query, a position of each term in relation to one
another, a comparison of a language of the document to the language
of the search query, and a comparison of a geographical local
associated with the document to the geographic local associated
with the search query. In one aspect of the technology described
herein, the final score may be computed using many individual
component scores of both the pre-computed components and the
real-time components that are computed after the search query is
received.
[0247] Referring now to FIG. 40, a flow diagram is provided
illustrating another method 4000, for instance using the
preliminary ranker 3810 of FIG. 38, to score a plurality of
documents based on relevance to a search query. At block 4002, a
table is accessed that stores data corresponding to a document. The
data is pre-computed to be indicative of the frequency of a term in
the document, although the data stored may not be the actual
frequency, but instead may be, for example, an IDF. This frequency
data contributes to a score of the document in relation to the
search query. For exemplary purposes only, pre-computed components
may comprise a frequency of terms in a document, a portion of the
document in which the terms are located, such as the title, body,
anchor, URL, abstract, etc., and how often the terms occur in those
portions of the document. At block 4004, scores for each of the
pre-computed components are computed, and at block 4006, scores for
each of the real-time components are computed in real-time, or at
runtime. At block 4008, a final score is computed for the document
in relation to the search query based on the scores for the
pre-computed and real-time components. As mentioned, the final
score may also consider click data in a click table. This click
data indicates how often the document is selected by other users
for the terms associated with the search query.
[0248] In addition to storing data in the score table for only a
portion of terms that are found in the document, such as those
terms that appear two or more times in the document, the
preliminary ranker is further adapted to use less memory than
typical rankers by allowing for collisions to occur when score
tables are built and accessed. A collision may occur, for example,
when data for one term found in a document is written over data for
another term. As such, the score table used in accordance with
aspects of the technology described herein operates much
differently than other score tables in a number of ways. Initially,
score tables typically have slots, each of which has a key
associated therewith. In one configuration, each term from the
document has its own key that is used when data for other terms is
being added to the table. Typically, when a slot already has data
stored therein, that slot is not used to store data for another
term, but instead another slot, such as an empty slot, is utilized.
However, the score tables used in configurations herein allow for
these collisions to occur. Collisions may occur when the score
tables are being built, such as by the score table building
component 3812, as well as when lookups occur, such as when the
score table lookup component 3814 accesses the score tables to
determine frequency and other pre-computed information for a
particular term in a document.
[0249] While typically a key may be stored as 64 bits, aspects of
the technology described herein provide for a much smaller amount
of bits to be stored. For example, in one configuration, just five
bits of a 64-bit key may be stored for a slot of a score table.
When five bits of a larger key is stored and is compared to another
five bit key, such as by the key comparison component 3818, there
is a higher chance that the keys will match than when a larger
amount of bits is stored and compared to other keys. While five
bits is used in the example above, it should be noted that any
number of bits may be used that is smaller than the total number of
bits. For instance, even using 60 bits of a 65 bit key would allow
for collisions to occur, as there would be a chance two different
keys would have the same 60 bit portion, and as such, in this case,
a collision would occur.
[0250] While collisions are allowed to occur, as described above,
precautions are taken to ensure that documents that are not
relevant to the search query, such as documents that the final
ranker would discard, are excluded from the set of documents sent
from the preliminary ranker to the final ranker. For example, when
a score table is being built for a particular document, and when it
has been determined that data for a second term found in a document
is to be added to a slot that already has been associated with a
first term that is different from the second term (e.g., the slot
already stores data associated with a different term), it may be
determined if the frequency of the second term in the document is
greater than the frequency of the first term in the document. If
the frequency of the second term is greater, that larger frequency
will be stored in the slot, but both terms will remain associated
with that same slot. Here, the frequency of the first term is being
rewritten with the frequency of the second term.
[0251] If, however, the frequency of the second term being added to
the slot is less than the frequency of the first term already
associated with the slot, the higher frequency of the first term
will remain stored in that slot, although the second term may be
added to that slot. Here, while the second term will be associated
with that slot, its frequency will not be stored in the score table
because it is lower than the frequency already stored. If the lower
frequency were to be stored over the higher frequency, the document
associated with that score table could be erroneously excluded for
a particular search query, even though it may be a relevant
document for the search query. By only storing the higher frequency
for both terms, the frequency returned or computed for one of the
terms may be higher than it should be (e.g., if the returned
frequency is for a different term), but the document will not be
excluded when it should have been returned in the set of relevant
documents sent to the subsequent ranker for further processing.
Instead, the document may be ranked higher than it should be, and
as such, may be returned in the set of relevant documents even if
it is not as relevant as the other documents. As such, all
documents found to be relevant, such as those having a score above
a particular threshold, will be returned, but some that may not be
as relevant may also be included.
[0252] Turning to FIG. 41, a flow diagram is provided illustrating
a method 4100, for instance using the preliminary ranker 3810 of
FIG. 38, for adding data for a term to slots of a score table.
Initially at block 4102, a table having slots is accessed, where
the table stores data associated with a document. At block 4104,
for a first slot of the table, a portion of a first hash key
associated with a first term is compared to a portion of a second
hash key associated with a second term that is to be added to the
first slot. As mentioned herein, aspects of the technology
described herein allow for more than one term to be associated with
the same slot, while only data indicative of the frequency of one
of the terms is stored therein. At block 4106, it is determined
whether the portion of the first hash key matches the portion of
the second hash key. If the portions of the hash keys do not match,
data (e.g., data indicative of frequency) corresponding to the
second term is not stored in the first slot of the score table,
shown at block 4112. If, however, the portions of the hash keys do
match, it is determined, at block 4108, that a frequency of the
second term in the document is greater than the frequency of the
first term in the document. At block 4110, data associated with the
frequency of the second term is stored in association with the
first slot of the table. In one configuration, this frequency data
rewrites the existing data, which corresponds to the frequency data
of the first term also associated with the first slot.
[0253] In accordance with aspects of the technology described
herein, if the portion of the first hash key does not match the
portion of the second hash key, a second slot is considered, and
thus the portion of the second hash key is compared to a portion of
a third hash key associated with a third term whose corresponding
data is stored in a second slot of the table. If these hash key
portions match, it is determined whether a frequency of the third
term in the document is greater than the frequency of the second
term. If the frequency of the second term is greater, the frequency
data associated with the second term is stored in the second slot
of the table, rewriting the frequency data associated with the
third term. If, however, the portion of the second hash key does
not match the portion of the third hash key, data corresponding to
the second term is not stored in the second slot of the table. This
process may continue until a slot is located where the portions of
the hash keys match.
[0254] Even though only data associated with a subset of terms
found in a document is stored in the score table, and even though
collisions are allowed to occur, results of the preliminary ranker
are unexpectedly better than traditional ranking systems, thus
providing a set of documents that are more relevant. For instance,
when the preliminary ranker is used in conjunction with a search
system, such as the search system described herein with respect to
FIG. 44, the documents that are found to be relevant by the
preliminary ranker are unexpectedly more relevant than documents
found to be relevant by other ranking systems, and are found much
faster. In some configurations, the preliminary ranker functions at
two times, or at five times, or at seven times, or even at ten
times faster than other ranking systems, enabling the entire search
system described herein to operate at a much faster rate than
traditional search systems.
[0255] In aspects of the technology described herein, the
preliminary ranker may be taught by a machine learning algorithm to
identify the most relevant documents for a particular search query.
Generally, the preliminary ranker is provided with input, which may
include search queries, documents, and which documents were found
by a human to be most relevant to each search query. From this
input, the preliminary ranker is trained to come up with the same
relevant documents as a human would. In one configuration, the
machine learning algorithm uses singular value decomposition, but
others may be used as well.
Match Fix-up
[0256] In a search system, such as the search system 4400 of FIG.
44, a matcher such as the matcher 4404, may be employed as an early
step in a search pipeline to identify matching documents based on
terms from a search query. As previously explained, the set of
documents identified by a matcher is, often times, too large to
return as search results or to send to an expensive ranker (i.e.,
expensive from the standpoint of the amount of processing required
to rank each document), such as the final ranker 4426 of FIG. 44,
since it would take too long for the ranker to process the large
number of documents. Additionally, if the matcher employs a
probabilistic approach, such as employing a bit vector-based search
index as described hereinabove, the set of matching documents may,
in fact, include one or more invalid matching documents, which are
not true matches for the search query. In other words, the invalid
matching documents may be false positives since those documents do
not contain one or more terms from the search query. Sending
invalid matching documents to an expensive ranker, such as the
final ranker 4426 of FIG. 44, would waste resources because of the
expense to process each document required by such a ranker.
[0257] To remove invalid matching documents and thereby reduce the
number of matching documents sent to a downstream ranker, some
aspects of the technology described herein employ what is referred
to herein as a match fix-up stage. Generally, a match fix-up
component, such as the match fix-up component 4424, may receive at
least a portion of a set of matching documents from a matcher, such
as the matcher 4404 of FIG. 44, that includes invalid matching
documents, and evaluates each document based on stored information
identifying terms contained in each document to remove at least
some of the invalid matching documents. The stored information may
be, for instance, a forward index.
[0258] A match fix-up component may be employed in a variety of
different locations between a matcher and a final ranker in
accordance with aspects of the technology described herein. As an
example, FIG. 44 illustrates a pipeline in which a matcher 4404
provides a set of matching documents 4420 that are evaluated using
a preliminary ranker 4422 to remove some irrelevant documents,
evaluated using the match fix-up component 4424 to remove at least
a portion of the invalid matching documents, and then evaluated
using a final ranker 4426 to provide a set of search results.
However, a search system may employ match fix-up at other locations
using any number of rankers. For instance, matching documents from
the matcher 4404 could be provided directly to the match fix-up
component 4424 without any preliminary ranker first removing
documents. Additionally, documents from the match fix-up component
4424 may be provided to one or more preliminary rankers before the
final ranker 4426. Any and all such variations are contemplated to
be within the scope of the technology described herein.
[0259] Resources used in search (e.g., cost, processing time,
storage, etc.) may be balanced with the need to provide accurate
and relevant search results in an efficient way. The use of a match
fix-up component may further optimize search results processes
without adding the need for additional resources and may,
ultimately, reduce resources currently used. Put simply, the match
fix-up component is intended to be a component that further refines
potential search results. The match fix-up component may provide
better performance with respect to filtering the potential search
results; but the match fix-up component may require additional
storage and may be slightly slower than the preliminary ranker.
However, any additional resources that may be used by the match
fix-up component (e.g., more expensive) may be offset or less than
resources that are spared by a subsequent expensive ranker, such as
the final ranker 4426 of FIG. 44. For example, by taking a little
more time to refine the set of documents at the match fix-up
component, less time will be needed by a subsequent ranker.
Further, a subsequent ranker may use less memory if the documents
received and refined by the subsequent ranker are narrower than
what would be received without match fix-up.
[0260] In application, a match fix-up component, such as the match
fix-up component 4426 of FIG. 44 may receive a set of documents
downstream from a matcher, such as the matcher 4404 (without or
without any filter using a preliminary ranker, such as the
preliminary ranker 4420, between the matcher and match fix-up
component). As mentioned, the set of documents may include invalid
matching documents. The inclusion of invalid matching documents at
this point is appropriate in the system since an objective is to
move quickly when appropriate, even if the results are slightly
off, and spend more time when appropriate to correct the results
and, thus, optimize the system and results. By adding a match
fix-up component, the set of documents sent on to a subsequent
ranker may be reduced and a preliminary ranker may be able to
perform its task a little faster, but a little less perfect, since
the match fix-up component may further refine the potential search
results. If potential search results were going directly from a
preliminary ranker to a final ranker without the use of match
fix-up, additional resources would need to be expended to ensure
that the potential search results sent to the final ranker were
very accurate (e.g., within 10% accuracy). Adding the match fix-up
component allows a preliminary ranker to not be as accurate and
perform faster.
[0261] As noted, the match fix-up component is particular useful
when a previous stage (e.g., the matcher) is based on information
theory-based compression. The match fix-up component may not be as
valuable in a system that does not have an information theory-based
compression engine such as, for example, a posting list since a
matcher using a posting list may be deterministic so there are not
invalid matching documents; meaning that the resources were
expended to get a perfect result so there is no opportunity for
match fix-up.
[0262] The match fix-up component may perform either lossless or
lossy fix-up. Lossless fix-up, as used herein, refers generally to
situations when original data can be perfectly reconstructed from
compressed data. Lossy fix-up, on the other hand, refers herein to
situations where inexact approximations are used to represent
content. The match fix-up component may, thus, fix-up perfectly or
less perfectly. Either choice may be compensated for in another
area. For instance, if the match fix-up component performs less
perfectly (e.g., a higher number of invalid matching documents are
sent on to a subsequent ranker than would be otherwise) then
additional bit vectors may be added in the matcher stage to reduce
the number of false positives (invalid matching documents) that are
sent on to the match fix-up component in the first place.
Alternatively, a perfect fix-up would allow the system to use fewer
bit vectors in the matcher stage while also being aware to not send
too many documents to the match fix-up component that would result
in too much cost at that stage. Thus, in that situation, a maximum
cost may be associated with a threshold number of documents such
that the matcher stage may have as few bit vectors as would allow
that a maximum number of documents, up to the threshold number of
documents, is sent on to the match fix-up component. This would
allow the cost at the match fix-up component to be below what is
designated and also allow the least amount of time and cost at the
matcher stage since the maximum number of documents that can be
sent are being sent.
[0263] Once the set of documents is received, the match fix-up
component may access a representation of each document within the
set of documents. The representation may be a data structure. The
representation may include a forward index for a document. The
forward index stores a list of one or more terms that are
present/associated with each document. The match fix-up component
may then compare the forward index with the search query to
determine whether the document is a valid matching document or an
invalid matching document. Valid matching documents are true
matches to a search query while invalid matching documents are not
true matches. Thus, the match fix-up component may review the
forward index to determine if the forward index for a document
indicates the document matches the search query (e.g., whether the
forward index for the document contains a first term or a second
term, etc.). Upon determining that one or more terms associated
with the search query are not present in a document, the match
fix-up component may identify the document as an invalid matching
document. Invalid matching documents may be discarded by the match
fix-up component and not sent on to the subsequent ranker.
Likewise, when one or more terms associated with a search query are
present in a forward index, the document associated with the
forward index may be identified as a valid matching document and
sent on to the subsequent ranker.
[0264] Typically, it would not be reasonable to evaluate a data
structure for each document to determine whether it is a valid or
invalid match. However, the use of the matcher and the preliminary
ranker in the present application reduce the number of possible
documents to a number that is acceptable to evaluate individually.
For instance, assume 100 documents are sent on the match fix-up
component and 50 are good and 50 are bad. The match fix-up
component may access, for instance, a storage location of the
document representations (e.g., SSD) and evaluate the
representation for each document. The entire document may be stored
in the SSD or, as an alternative, every n number of words may be
stored in the SSD (where n is any number). The amount of the
document stored is configurable based on, for example, design
goals, tradeoffs between the matcher and the match fix-up
component, and the like.
[0265] The introduction of the match fix-up component provides
opportunities for the system to be more efficient by allowing
stages preceding the match fix-up (e.g., the matcher and
preliminary ranker) to perform worse than they were without match
fix-up. Additionally opportunities to optimize the system exist
such as evaluating a cost of error rates versus a cost of memory.
For example, if for a particular system it is identified that the
cost of 10% error rate is 1 gb and the cost of 20% error rate is 2
gb then the system can be optimized to perform at an error rate
that is still efficient but utilizes an optimal memory so that the
total amount of memory/resources uses is below the uncompressed
value.
[0266] Turning now to FIG. 42, a flow diagram is provided
illustrating a method 4200 for employing match fix-up to remove
invalid matching documents downstream from a probabilistic matcher.
The method 4200 may be performed at least partially using, for
instance, the match fix-up component 4424 of FIG. 44. Initially, at
block 4202, a plurality of documents found to be relevant to at
least a portion of a search query is received. The plurality of
documents may include one or more invalid matching documents. At
block 4204, a representation for each document is accessed. The
representation for a document includes terms present within the
document. At block 4206, the terms present within each document are
compared to one or more terms associated with the search query. At
block 4208, it is determined that the one or more invalid matching
documents do not contain the one or more terms associated with the
query. At block 4210, upon determining that the one or more invalid
matching documents do not contain the one or more terms associated
with the query, the one or more invalid matching documents are
removed from the plurality of documents found to be relevant to the
at least a portion of the search query.
[0267] Turning now to FIG. 43, a flow diagram is provided
illustrating another method 4300 for employing match fix-up to
remove invalid matching documents downstream from a probabilistic
matcher. The method 4300 may be performed at least partially using,
for instance, the match fix-up component 4424 of FIG. 44.
Initially, at block 4302, a first plurality of documents found to
be relevant to at least a portion of a search query is received.
The first plurality of documents may include one or more invalid
matching documents. At block 4304, a forward index for each
document of the first plurality of documents is accessed. The
forward index may store a list of one or more terms contained in
each document. At block 4306, using the forward index for each
document of the first plurality of documents, one or more valid
matching documents that contain one or more terms associated with
the search query is identified while at block 4308, using the
forward index for each document of the first plurality of
documents, one or more invalid matching documents that do not
contain the one or more terms associated with the search query is
identified. At block 4310, the one or more invalid matching
documents is removed from the first plurality of documents to
create a filtered set of one or more documents found to be relevant
to the at least a portion of the search query. At block 4312, the
filtered set of one or more documents found to be relevant to the
at least a portion of the search query is communicated for ranking
each document of the filtered set of one or more documents for the
search query.
Bit Vector Search System Configurations
[0268] The use of a bit vector based search index, preliminary
ranker, and match fix-up as discussed hereinabove allows for
various configurations depending on design goals. The data used by
each stage is needed for a decreasing the number of documents for
subsequent consideration, so the bit vector data used by the
matcher is optimized for inexpensive reduction of the set of all
possible documents. However, these bit vectors are populated based
on information value, so compression of the size of the bit vector
memory simply increases the false positive rate. False positive
rate is halved by increasing the buffer by a fixed size
(log-linear). False positive results are finally removed at the
match fix-up stage, and there is a fixed cost for each false
positive removed. Preliminary ranking is a fixed cost per item
scored (e.g., approximately 150 ns per document per thread if the
score data used by the preliminary ranker is resident in
memory)
[0269] Below are examples of five different configurations based on
different design goals to illustrate the elasticity of a bit
vector-based search system. As discussed previously, "D" refers to
storage consumption (e.g., the number of documents that may be
indexed per machine) and "Q" refers to processing speed (e.g.,
queries per second--QPS). Table 1 provides a summary of the
configurations.
TABLE-US-00001 TABLE 1 Configurations Score Data for Match Prelim-
Data for Config- inary Match uration Bit Vectors Ranker Fix-Up
Particulars High DQ Low DDR None. 10M @ 18K compression memory Low
False QPS DDR positive rate Freshness tier memory is fixed by L2
High DQ DDR. SSD DDR SSD 50M @ 4K (with SSD) for phrases QPS Highly
All tiers compressed High Q Low DDR DDR 10M @ 18K compression
memory Memory QPS DDR Low latency memory search svc for graphs,
queues or objects High D High SSD SSD 500M @ 50 compression memory
memory QPS SSD Partitioned for memory personal or shared docs Deep
D High HDD HDD 2B++ @ 1 compression memory memory QPS HDD Deep
archive memory Deep DQ Extreme HDD HDD 1B++ @ 100 compression
memory memory results per SSD memory 1 seek second (tail ids region
of per match web queries) HDD to scan
[0270] 1. High DQ--Efficient Web search
[0271] A High DQ configuration maximizes total efficiency. This
configuration is limited by the DDR bus throughput rate. This
approach was ran at 180K DQ, with 10 million documents per machine
at 18K QPS on a V13 machine. The version with SSD is still limited
by the DDR bus, but uses SSD to remove pressure for the DDR
capacity, thus allowing for 5 times the document count at one fifth
the speed. There are numerous performance improvements in the
pipeline that involve tighter control of the query planning and
more aggressive early termination. These changes could each
increase performance by another 25%. Early termination is used to
limit the cost of a query, in a way that minimizes damage to the
relevance of the result set.
[0272] 2. Deep DQ--Tail Web search
[0273] Deep DQ can operate on the same box as High DQ without
significant impact to the head search capability, although this
argument will be stronger when faster SSDs are available. Deep DQ
primarily is using HDD, although it does use very narrow bit
vectors in SSD to find areas of HDD to scan. It uses tuples and
phrases to avoid low IDF terms (equivalent of long posting lists).
A HDD seek occurs for each result. With a 1T web index, 1000
machines can hold the internet. This approach is intended for
queries that are unlikely to find many results, or many deep DQ bit
vectors are needed.
[0274] 3. High Q--SubSQL
[0275] The high Q configuration is similar to the high DQ
configuration, except that it does not use SSD. Without SSD the
engine is configured to have consistently low latency. Even
difficult graph queries like "List all of the friends of Madonna"
would complete in under 10 ms, and most will complete in 500
usec.
[0276] This configuration may be designed to work within Object
Store Natively, such that the combined entity has many of the
capabilities of NoSQL (especially Document stores like MongoDB, the
most popular NoSQL software).
[0277] SubSQL moves further away from typical SQL by providing low
level high performance primitives, as opposed to generalized
interfaces to data. For example, a Join operation is not performed
by SubSQ; however, complex join-like capability can be built into
an index to provide low latency and high performance cloud
operations. Finer grained ranking and sorting operations are
primarily used in SubSQL as a way to inexpensively discover items
within a large result set.
[0278] 4. High D--Digital Life and Digital Work.
[0279] The world of personal documents and personal emails is going
to intersect with graphs which are going to both allow sharing of
more content along a graph, but also help each of us find what we
need without asking for it. This configuration may integrate graphs
(served by SubSQL) with documents served by a High D search engine.
Each machine holds a ton of documents, but does not serve them very
quickly. This works very well for unshared personal documents,
because a single machine can hold all of a single person's
documents, and a query only needs to access that single machine.
Each person executes few queries per day, and 100,000 people can be
shared tenants on a single machine.
[0280] The big breakthrough happens when people share documents
with each other. When a person queries for a document, the search
usually will need to look through the documents of anybody who may
have shared documents with me. People are partitioned with affinity
to their graphs, and people who are sharing documents very broadly
are replicated on many partitions.
General Operating Environment
[0281] Having briefly described an overview of aspects of the
technology described herein, an exemplary operating environment in
which aspects of the technology described herein may be implemented
is described below in order to provide a general context for
various aspects of the technology described herein. Referring
initially to FIG. 45 in particular, an exemplary operating
environment for implementing aspects of the technology described
herein is shown and designated generally as computing device 4300.
Computing device 4300 is but one example of a suitable computing
environment and is not intended to suggest any limitation as to the
scope of use or functionality of aspects of the technology
described herein. Neither should the computing device 100 be
interpreted as having any dependency or requirement relating to any
one or combination of components illustrated.
[0282] Aspects of the technology provided herein may be described
in the general context of computer code or machine-useable
instructions, including computer-executable instructions such as
program modules, being executed by a computer or other machine,
such as a personal data assistant or other handheld device.
Generally, program modules including routines, programs, objects,
components, data structures, etc., refer to code that perform
particular tasks or implement particular abstract data types.
Aspects of the technology described herein may be practiced in a
variety of system configurations, including hand-held devices,
consumer electronics, general-purpose computers, more specialty
computing devices, etc. Aspects of the technology described herein
may also be practiced in distributed computing environments where
tasks are performed by remote-processing devices that are linked
through a communications network.
[0283] With reference to FIG. 45, computing device 4500 includes a
bus 4510 that directly or indirectly couples the following devices:
memory 4512, one or more processors 4514, one or more presentation
components 4516, input/output (I/O) ports 4518, input/output
components 4520, and an illustrative power supply 4522. Bus 4510
represents what may be one or more busses (such as an address bus,
data bus, or combination thereof). Although the various blocks of
FIG. 45 are shown with lines for the sake of clarity, in reality,
delineating various components is not so clear, and metaphorically,
the lines would more accurately be grey and fuzzy. For example, one
may consider a presentation component such as a display device to
be an I/O component. Also, processors have memory. The inventors
recognize that such is the nature of the art, and reiterate that
the diagram of FIG. 45 is merely illustrative of an exemplary
computing device that can be used in connection with one or more
aspects of the technology described herein. Distinction is not made
between such categories as "workstation," "server," "laptop,"
"hand-held device," etc., as all are contemplated within the scope
of FIG. 45 and reference to "computing device."
[0284] Computing device 4500 typically includes a variety of
computer-readable media. Computer-readable media can be any
available media that can be accessed by computing device 4500 and
includes both volatile and nonvolatile media, removable and
non-removable media. By way of example, and not limitation,
computer-readable media may comprise computer storage media and
communication media. Computer storage media includes both volatile
and nonvolatile, removable and non-removable media implemented in
any method or technology for storage of information such as
computer-readable instructions, data structures, program modules or
other data. Computer storage media includes, but is not limited to,
RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM,
digital versatile disks (DVD) or other optical disk storage,
magnetic cassettes, magnetic tape, magnetic disk storage or other
magnetic storage devices, or any other medium which can be used to
store the desired information and which can be accessed by
computing device 4500. Computer storage media does not comprise
signals per se. Communication media typically embodies
computer-readable instructions, data structures, program modules or
other data in a modulated data signal such as a carrier wave or
other transport mechanism and includes any information delivery
media. The term "modulated data signal" means a signal that has one
or more of its characteristics set or changed in such a manner as
to encode information in the signal. By way of example, and not
limitation, communication media includes wired media such as a
wired network or direct-wired connection, and wireless media such
as acoustic, RF, infrared and other wireless media. Combinations of
any of the above should also be included within the scope of
computer-readable media.
[0285] Memory 4512 includes computer-storage media in the form of
volatile and/or nonvolatile memory. The memory may be removable,
non-removable, or a combination thereof. Exemplary hardware devices
include solid-state memory, hard drives, optical-disc drives, etc.
Computing device 4500 includes one or more processors that read
data from various entities such as memory 4512 or I/O components
4520. Presentation component(s) 4516 present data indications to a
user or other device. Exemplary presentation components include a
display device, speaker, printing component, vibrating component,
etc.
[0286] I/O ports 4518 allow computing device 4500 to be logically
coupled to other devices including I/O components 4520, some of
which may be built in. Illustrative components include a
microphone, joystick, game pad, satellite dish, scanner, printer,
wireless device, etc. The I/O components 4520 may provide a natural
user interface (NUI) that processes air gestures, voice, or other
physiological inputs generated by a user. In some instance, inputs
may be transmitted to an appropriate network element for further
processing. A NUI may implement any combination of speech
recognition, touch and stylus recognition, facial recognition,
biometric recognition, gesture recognition both on screen and
adjacent to the screen, air gestures, head and eye tracking, and
touch recognition associated with displays on the computing device
4500. The computing device 4500 may be equipped with depth cameras,
such as, stereoscopic camera systems, infrared camera systems, RGB
camera systems, and combinations of these for gesture detection and
recognition. Additionally, the computing device 4500 may be
equipped with accelerometers or gyroscopes that enable detection of
motion. The output of the accelerometers or gyroscopes may be
provided to the display of the computing device 4500 to render
immersive augmented reality or virtual reality.
[0287] The technology has been described in relation to particular
aspects, which are intended in all respects to be illustrative
rather than restrictive. Alternative configurations will become
apparent to those of ordinary skill in the art to which the
technology described herein pertains without departing from its
scope.
[0288] From the foregoing, it will be seen that the technology
described herein is well adapted to attain all the ends and objects
set forth above, together with other advantages which are obvious
and inherent to the system and method. It will be understood that
certain features and subcombinations are of utility and may be
employed without reference to other features and subcombinations.
This is contemplated by and is within the scope of the claims.
* * * * *